{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDataSet(filename):\n",
    "    '''\n",
    "    加载数据集\n",
    "    '''\n",
    "    dataList = []\n",
    "    with open(filename) as fr:\n",
    "        for line in fr:\n",
    "            curLine = line.strip().split('\\t')\n",
    "            fltLine = list(map(float, curLine))\n",
    "            dataList.append(fltLine)\n",
    "    return np.mat(dataList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distEclud(vecA, vecB):\n",
    "    '''\n",
    "    计算两个向量间的欧氏距离\n",
    "    Args:\n",
    "        vecA: 向量A\n",
    "        vecB: 向量B\n",
    "    Return:\n",
    "        距离\n",
    "    '''\n",
    "    return np.sqrt(np.sum(np.power(vecA-vecB, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randCent(dataSet, k):\n",
    "    '''\n",
    "    构建簇质心\n",
    "    Args:\n",
    "        dataSet: 数据集\n",
    "        k: 簇个数\n",
    "    Return:\n",
    "        centroids: 簇质心\n",
    "    '''\n",
    "    n = np.shape(dataSet)[1]\n",
    "    centroids = np.mat(np.zeros((k,n)))\n",
    "    for j in range(n):\n",
    "        minJ = np.min(dataSet[:,j])\n",
    "        rangeJ = float(np.max(dataSet[:,j]) - minJ)\n",
    "        # 保证随机生成的质心在整个数据集的边界之内\n",
    "        centroids[:,j] = minJ + np.random.rand(k,1) * rangeJ  \n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "datMat = loadDataSet('data/testSet.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.379713 4.838138\n",
      "-4.232586 5.1904\n"
     ]
    }
   ],
   "source": [
    "print(np.min(datMat[:,0]), np.max(datMat[:,0]))\n",
    "print(np.min(datMat[:,1]), np.max(datMat[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.42484169  4.04400593]\n",
      " [-2.65608862 -3.02489749]]\n",
      "5.184632816681332\n"
     ]
    }
   ],
   "source": [
    "print(randCent(datMat, 2))\n",
    "print(distEclud(datMat[0], datMat[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):\n",
    "    '''\n",
    "    k均值聚类算法\n",
    "    Args:\n",
    "        dataSet: 数据集\n",
    "        k：需要聚类的簇的个数\n",
    "        distMeas=distEclud：距离度量函数\n",
    "        createCent=randCent：随机生成簇质心函数\n",
    "    Return:\n",
    "        centroids: 最终簇质心\n",
    "        clusterAssment: 最终点聚类结果\n",
    "    '''\n",
    "    m = np.shape(dataSet)[0]\n",
    "    # 第一个保存所属质心，第二个保存距离\n",
    "    clusterAssment = np.mat(np.zeros((m,2)))\n",
    "    centroids = createCent(dataSet, k)\n",
    "    clusterChanged = True\n",
    "    while clusterChanged:\n",
    "        clusterChanged = False\n",
    "        # 将样本分配到相应的簇中\n",
    "        for i in range(m):\n",
    "            minDist = np.inf\n",
    "            minIndex = -1\n",
    "            for j in range(k):\n",
    "                distJI = distMeas(centroids[j,:], dataSet[i,:])\n",
    "                if distJI < minDist:\n",
    "                    minDist = distJI\n",
    "                    minIndex = j\n",
    "            if clusterAssment[i,0] != minIndex:\n",
    "                clusterChanged = True\n",
    "            clusterAssment[i,:] = minIndex, minDist ** 2\n",
    "        print(centroids)\n",
    "        # 更新簇质心\n",
    "        for cent in range(k):\n",
    "            ptsInClust = dataSet[np.nonzero(clusterAssment[:,0].A == cent)[0]]\n",
    "            centroids[cent,:] = np.mean(ptsInClust, axis=0)\n",
    "    return centroids, clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.43150932  0.29178038]\n",
      " [-1.2999912   3.7987273 ]\n",
      " [-1.55067675  2.31773045]\n",
      " [-2.19867033 -2.34525372]]\n",
      "[[ 2.96911125 -0.3260355 ]\n",
      " [ 0.4590062   3.9883923 ]\n",
      " [-2.60208725  2.57286356]\n",
      " [-3.01169468 -3.01238673]]\n",
      "[[ 3.10012512 -1.31169504]\n",
      " [ 2.06470057  3.64318664]\n",
      " [-2.54951105  2.75812458]\n",
      " [-3.01169468 -3.01238673]]\n",
      "[[ 2.8692781  -2.54779119]\n",
      " [ 2.54391447  3.21299611]\n",
      " [-2.46154315  2.78737555]\n",
      " [-3.38237045 -2.9473363 ]]\n",
      "[[ 2.80293085 -2.7315146 ]\n",
      " [ 2.6265299   3.10868015]\n",
      " [-2.46154315  2.78737555]\n",
      " [-3.38237045 -2.9473363 ]]\n"
     ]
    }
   ],
   "source": [
    "dataMat = loadDataSet('data/testSet.txt')\n",
    "myCentroids, clusterAssing = kMeans(dataMat, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "def showPlt(datMat, alg=kMeans, numClust=4):\n",
    "    myCentroids, clustAssing = alg(datMat, numClust)\n",
    "    fig = plt.figure()\n",
    "    rect=[0.1,0.1,0.8,0.8]\n",
    "    scatterMarkers=['s', 'o', '^', '8', 'p', \\\n",
    "                    'd', 'v', 'h', '>', '<']\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    ax0=fig.add_axes(rect, label='ax0', **axprops)\n",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[np.nonzero(clustAssing[:,0].A==i)[0],:]\n",
    "        markerStyle = scatterMarkers[i % len(scatterMarkers)]\n",
    "        ax1.scatter(ptsInCurrCluster[:,0].flatten().A, ptsInCurrCluster[:,1].flatten().A, marker=markerStyle, s=90)\n",
    "    ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.27705439  1.62728646]\n",
      " [ 4.11157411 -1.89365486]\n",
      " [ 2.44470421 -3.27556843]\n",
      " [-4.0166081  -1.81543428]]\n",
      "[[-0.00480723  2.9078739 ]\n",
      " [ 4.5110462  -1.0349174 ]\n",
      " [ 2.28769    -3.23832819]\n",
      " [-3.53973889 -2.89384326]]\n",
      "[[-0.21449573  3.03339168]\n",
      " [ 4.22786929 -0.08794886]\n",
      " [ 2.19454347 -3.07604306]\n",
      " [-3.53973889 -2.89384326]]\n",
      "[[-0.59196673  3.13360545]\n",
      " [ 3.74487682  0.74644273]\n",
      " [ 2.19454347 -3.07604306]\n",
      " [-3.53973889 -2.89384326]]\n",
      "[[-1.4837585   3.05005908]\n",
      " [ 3.26868071  2.05182465]\n",
      " [ 2.33200728 -3.04752428]\n",
      " [-3.53973889 -2.89384326]]\n",
      "[[-2.46154315  2.78737555]\n",
      " [ 2.6265299   3.10868015]\n",
      " [ 2.65077367 -2.79019029]\n",
      " [-3.53973889 -2.89384326]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24394eeba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPlt(datMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def biKmeans(dataSet, k, distMeas = distEclud):\n",
    "    '''\n",
    "    二分k-均值聚类算法\n",
    "    Args:\n",
    "        dataSet: 数据集\n",
    "        k：簇个数\n",
    "        distMeas = distEclud：距离度量函数\n",
    "    Return:\n",
    "        centList: 簇质心\n",
    "        clusterAssment: 聚类结果\n",
    "    '''\n",
    "    m = np.shape(dataSet)[0]\n",
    "    clusterAssment = np.mat(np.zeros((m,2)))\n",
    "    # 创建初始簇\n",
    "    centroid0 = np.mean(dataSet, axis=0).tolist()[0]\n",
    "    centList = [centroid0]\n",
    "    for j in range(m):\n",
    "        clusterAssment[j,1] = distMeas(np.mat(centroid0), dataSet[j,:])**2\n",
    "    while len(centList) < k:\n",
    "        lowestSSE = np.inf\n",
    "        # 尝试划分每一簇\n",
    "        for i in range(len(centList)):\n",
    "            ptsInCurrCluster = dataSet[np.nonzero(clusterAssment[:,0].A == i)[0],:]\n",
    "            centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas)\n",
    "            # 划分后的误差平方和\n",
    "            sseSplit = np.sum(splitClustAss[:,1])\n",
    "            sseNotSplit = np.sum(clusterAssment[np.nonzero(clusterAssment[:,0].A != i)[0],1])\n",
    "            print('sseSplit, and notSplit:',sseSplit, sseNotSplit)\n",
    "            if sseSplit + sseNotSplit < lowestSSE:\n",
    "                bestCentToSplit = i\n",
    "                bestNewCents = centroidMat\n",
    "                bestClustAss = splitClustAss.copy()\n",
    "                lowestSSE = sseSplit + sseNotSplit\n",
    "        bestClustAss[np.nonzero(bestClustAss[:,0].A == 1)[0], 0] = len(centList)\n",
    "        bestClustAss[np.nonzero(bestClustAss[:,0].A == 0)[0], 0] = bestCentToSplit\n",
    "        print('the bestCentToSplit is:', bestCentToSplit)\n",
    "        print('the len of bestClustAss is:', len(bestClustAss))\n",
    "        centList[bestCentToSplit] = bestNewCents[0,:]\n",
    "        centList.append(bestNewCents[1,:])\n",
    "        clusterAssment[np.nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:] = bestClustAss \n",
    "    return np.mat(np.array(centList)), clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataMat2 = loadDataSet('data/testSet2.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.70109905  2.89447515]\n",
      " [-4.11901257 -2.03989761]]\n",
      "[[-0.00675605  3.22710297]\n",
      " [-0.45965615 -2.7782156 ]]\n",
      "sseSplit, and notSplit: 453.0334895807502 0.0\n",
      "the bestCentToSplit is: 0\n",
      "the len of bestClustAss is: 60\n",
      "[[ 1.47967183  3.83821849]\n",
      " [-0.26706624  0.88718608]]\n",
      "[[ 2.49767838  3.34813876]\n",
      " [-2.77481516  3.09332658]]\n",
      "[[ 2.93386365  3.12782785]\n",
      " [-2.94737575  3.3263781 ]]\n",
      "sseSplit, and notSplit: 77.59224931775066 29.15724944412535\n",
      "[[ 0.17427503 -1.08851642]\n",
      " [-0.86027004 -3.84853099]]\n",
      "[[-0.6985095  -1.490984  ]\n",
      " [-0.39994281 -3.1000235 ]]\n",
      "[[-1.3776246  -1.6522424 ]\n",
      " [-0.15366667 -3.15354   ]]\n",
      "[[-1.41084317 -1.873139  ]\n",
      " [-0.05200457 -3.16610557]]\n",
      "[[-1.31198114e+00 -1.96162114e+00]\n",
      " [-7.11923077e-04 -3.21792031e+00]]\n",
      "[[-1.26873575 -2.07139688]\n",
      " [ 0.07973025 -3.24942808]]\n",
      "[[-1.26405367 -2.209896  ]\n",
      " [ 0.19848727 -3.24320436]]\n",
      "[[-1.1836084 -2.2507069]\n",
      " [ 0.2642961 -3.3057243]]\n",
      "[[-1.12616164 -2.30193564]\n",
      " [ 0.35496167 -3.36033556]]\n",
      "sseSplit, and notSplit: 12.753263136887313 423.8762401366249\n",
      "the bestCentToSplit is: 0\n",
      "the len of bestClustAss is: 40\n"
     ]
    }
   ],
   "source": [
    "centList,myNewAssment = biKmeans(dataMat2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 2.93386365,  3.12782785],\n",
       "        [-0.45965615, -2.7782156 ],\n",
       "        [-2.94737575,  3.3263781 ]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-3.94455009  2.13846316]\n",
      " [-3.32765103  0.4934085 ]]\n",
      "[[-1.62922896  3.46284893]\n",
      " [ 1.04623688 -0.60536709]]\n",
      "[[-1.47714714  3.46545543]\n",
      " [ 0.99677359 -0.73477953]]\n",
      "[[-1.3277349   3.46607079]\n",
      " [ 0.93680474 -0.87084665]]\n",
      "[[-0.84735206  3.46862312]\n",
      " [ 0.63042504 -1.33843332]]\n",
      "[[-0.26853357  3.36606168]\n",
      " [ 0.02053813 -2.21845543]]\n",
      "[[-0.06953469  3.29844341]\n",
      " [-0.32150057 -2.62473743]]\n",
      "[[-0.00675605  3.22710297]\n",
      " [-0.45965615 -2.7782156 ]]\n",
      "sseSplit, and notSplit: 453.0334895807502 0.0\n",
      "the bestCentToSplit is: 0\n",
      "the len of bestClustAss is: 60\n",
      "[[2.32581476 2.5230005 ]\n",
      " [2.46452873 1.6514989 ]]\n",
      "[[-0.36271239  3.39917483]\n",
      " [ 3.196851    1.67845625]]\n",
      "[[-2.04192752  3.38503076]\n",
      " [ 3.3851964   2.96389   ]]\n",
      "[[-2.94737575  3.3263781 ]\n",
      " [ 2.93386365  3.12782785]]\n",
      "sseSplit, and notSplit: 77.59224931775066 29.15724944412535\n",
      "[[ 0.72540847 -1.97588583]\n",
      " [-0.86676441 -1.4797768 ]]\n",
      "[[ 0.35496167 -3.36033556]\n",
      " [-1.12616164 -2.30193564]]\n",
      "sseSplit, and notSplit: 12.753263136887313 423.8762401366249\n",
      "the bestCentToSplit is: 0\n",
      "the len of bestClustAss is: 40\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24393bd85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPlt(dataMat2, alg=biKmeans, numClust=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib\n",
    "import json\n",
    "def geoGrab(stAddress, city):\n",
    "    apiStem = 'http://where.yahooapis.com/geocode?'  #create a dict and constants for the goecoder\n",
    "    params = {}\n",
    "    params['flags'] = 'J'#JSON return type\n",
    "    params['appid'] = 'aaa0VN6k'\n",
    "    params['location'] = '%s %s' % (stAddress, city)\n",
    "    url_params = urllib.parse.urlencode(params)\n",
    "    yahooApi = apiStem + url_params      #print url_params\n",
    "    print(yahooApi)\n",
    "    c=urllib.request.urlopen(yahooApi)\n",
    "    return json.loads(c.read())\n",
    "\n",
    "from time import sleep\n",
    "def massPlaceFind(fileName):\n",
    "    fw = open('places.txt', 'w')\n",
    "    for line in open(fileName).readlines():\n",
    "        line = line.strip()\n",
    "        lineArr = line.split('\\t')\n",
    "        retDict = geoGrab(lineArr[1], lineArr[2])\n",
    "        if retDict['ResultSet']['Error'] == 0:\n",
    "            lat = float(retDict['ResultSet']['Results'][0]['latitude'])\n",
    "            lng = float(retDict['ResultSet']['Results'][0]['longitude'])\n",
    "            print(\"%s\\t%f\\t%f\" % (lineArr[0], lat, lng))\n",
    "            fw.write('%s\\t%f\\t%f\\n' % (line, lat, lng))\n",
    "        else: print(\"error fetching\")\n",
    "        sleep(1)\n",
    "    fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distSLC(vecA, vecB):#Spherical Law of Cosines\n",
    "    a = np.sin(vecA[0,1]*np.pi/180) * np.sin(vecB[0,1]*np.pi/180)\n",
    "    b = np.cos(vecA[0,1]*np.pi/180) * np.cos(vecB[0,1]*np.pi/180) * \\\n",
    "                      np.cos(np.pi * (vecB[0,0]-vecA[0,0]) /180)\n",
    "    return np.arccos(a + b)*6371.0 #pi is imported with numpy\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "def clusterClubs(numClust=5):\n",
    "    datList = []\n",
    "    for line in open('data\\places.txt').readlines():\n",
    "        lineArr = line.split('\\t')\n",
    "        datList.append([float(lineArr[4]), float(lineArr[3])])\n",
    "    datMat = np.mat(datList)\n",
    "    myCentroids, clustAssing = biKmeans(datMat, numClust, distMeas=distSLC)\n",
    "    fig = plt.figure()\n",
    "    rect=[0.1,0.1,0.8,0.8]\n",
    "    scatterMarkers=['s', 'o', '^', '8', 'p', \\\n",
    "                    'd', 'v', 'h', '>', '<']\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    ax0=fig.add_axes(rect, label='ax0', **axprops)\n",
    "    imgP = plt.imread('data\\Portland.png')\n",
    "    ax0.imshow(imgP)\n",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[np.nonzero(clustAssing[:,0].A==i)[0],:]\n",
    "        markerStyle = scatterMarkers[i % len(scatterMarkers)]\n",
    "        ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90)\n",
    "    ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-122.41820228   45.5933652 ]\n",
      " [-122.73541372   45.64369176]]\n",
      "[[-122.55202429   45.51080603]\n",
      " [-122.69665539   45.51353092]]\n",
      "[[-122.55095081   45.5149589 ]\n",
      " [-122.69753113   45.51014305]]\n",
      "[[-122.54868607   45.51882187]\n",
      " [-122.69551477   45.50729503]]\n",
      "sseSplit, and notSplit: 3043.2633161055337 0.0\n",
      "the bestCentToSplit is: 0\n",
      "the len of bestClustAss is: 69\n",
      "[[-122.59047086   45.55635261]\n",
      " [-122.53964965   45.43854204]]\n",
      "[[-122.56608835   45.5346873 ]\n",
      " [-122.5138815    45.487091  ]]\n",
      "[[-122.57746344   45.53534433]\n",
      " [-122.50552      45.49403817]]\n",
      "[[-122.57768158   45.53263337]\n",
      " [-122.49860291   45.49496564]]\n",
      "sseSplit, and notSplit: 464.7205983452951 2191.824427523823\n",
      "[[-122.76779444   45.50819069]\n",
      " [-122.67171427   45.47026008]]\n",
      "[[-122.74941346   45.545862  ]\n",
      " [-122.66856542   45.48801154]]\n",
      "sseSplit, and notSplit: 1489.2150612291607 851.4388885817106\n",
      "the bestCentToSplit is: 1\n",
      "the len of bestClustAss is: 39\n",
      "[[-122.38490132   45.47996718]\n",
      " [-122.54064415   45.49676216]]\n",
      "[[-122.4009285    45.46897   ]\n",
      " [-122.55924018   45.52238271]]\n",
      "sseSplit, and notSplit: 501.3287882204044 1489.2150612291607\n",
      "[[-122.76986715   45.55442929]\n",
      " [-122.75714297   45.59565361]]\n",
      "[[-122.77401914   45.50119986]\n",
      " [-122.72070683   45.59796783]]\n",
      "[[-122.78288433   45.4942305 ]\n",
      " [-122.72072414   45.59011757]]\n",
      "sseSplit, and notSplit: 207.99615747198953 1689.515066958223\n",
      "[[-122.69654314   45.50504428]\n",
      " [-122.67141465   45.4604971 ]]\n",
      "[[-122.66041321   45.52084007]\n",
      " [-122.67807633   45.44971158]]\n",
      "[[-122.65576353   45.51592212]\n",
      " [-122.69274678   45.43529156]]\n",
      "sseSplit, and notSplit: 316.2253437917487 1502.577771434359\n",
      "the bestCentToSplit is: 2\n",
      "the len of bestClustAss is: 26\n",
      "[[-122.57489747   45.48935456]\n",
      " [-122.41746508   45.54986626]]\n",
      "[[-122.56208315   45.52250274]\n",
      " [-122.42811233   45.485694  ]]\n",
      "sseSplit, and notSplit: 511.80653761865034 967.364226644397\n",
      "[[-122.67236042   45.57064975]\n",
      " [-122.81465988   45.63920734]]\n",
      "[[-122.74162142   45.53744792]\n",
      " [-122.842918     45.646831  ]]\n",
      "sseSplit, and notSplit: 457.29813332506467 1167.6642323734595\n",
      "[[-122.65808159   45.48792599]\n",
      " [-122.63999318   45.4868839 ]]\n",
      "[[-122.66738409   45.52019509]\n",
      " [-122.63445917   45.50808833]]\n",
      "[[-122.66572991   45.52369627]\n",
      " [-122.63749183   45.5016695 ]]\n",
      "[[-122.66265783   45.52442375]\n",
      " [-122.6392172    45.4955182 ]]\n",
      "sseSplit, and notSplit: 30.34080025802466 1740.223612761877\n",
      "[[-122.65236043   45.42181405]\n",
      " [-122.70300469   45.42867032]]\n",
      "[[-122.63804575   45.43816325]\n",
      " [-122.7365076    45.4329942 ]]\n",
      "sseSplit, and notSplit: 105.71051898445806 1581.15727389859\n",
      "the bestCentToSplit is: 0\n",
      "the len of bestClustAss is: 30\n"
     ]
    },
    {
     "data": {
      "image/png": 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W19eikIWCr54IDXnFE1FZK53xVuD8eOmqbQHGk2kSez7Pa7kMTOvEOsx+roxvhsrkQX+clFECiGaPXnfwqT7lZV1d6jfJZFKkUjr5XIb6evj1TWcG5VKBSqWUWLopTUuEk+jNcRz3TEx58f9a7Wyu6eAgvPa9/SPuP3jESy8+u1RBpOkazVYXURLRdQ3H8cikU0iShCSK51qsn/9c+Axtba6jKjLFYgFVUSgWcsnzZTsughAmwh3HZbNe5dbNbarVMvl0mnw6THZkM2n02MMSRdZrYW4mFlbr65Uw4a6pYYOj75P2h3xkhbmH2NNKp/TEo/CD4EwTIoTPmOu6BEGAbTs4jovrhj+j0Zj53Dq7E6Hw3NioLa0dQRDwPT/5+UVgPp/TbHaunLeofPV3qHz1d/A9/1zFsYh4fW7Wa2xs1HAcl/ncYjoNw7wXeWqxR/g0TQW/bNjqfwD+K5a9BoB/LAjCXwmC8N8LgrBKvT8HDAVB+D+ikNd/JwiCdIX9Px5Cj+M7wPPAaXM+F73+Z9F2nxhiyzXwA+6U87hBsFIpbG/Vlx5qy7TY2Vld/nheOKpQyCWNdNnsxd3f58GKlEc+yq3MH5x4DxsbJ4tHNhrMrdUC4heFclpfEi7FYh7bdtg/PKbfH1HIZxmNJ9iWTTaTQldVDMMkm89h2SdhhTgXsYjF/wM/SBZx0ncQBNy9d5/dG5vcOFWf3+8PMQwTK6ruyuUyCEKYcJ0ZczY2avhBQLvdWzp/y7RoNjvkNI/v/VUL23bodHrYjsNwNKHd7iXbypJMOn22u3tshDZYPptBVRU836dYyCGIIkcLXkesPNudHoOogkwUIec22N6qs71VZ/fGJrs3NplMDbY21/EFmfyKsOtgMMZ1XAaDMZ7vo6oKiiIjy+HPxkbtXCt/lfAMggBREpOfXwSMuQmCsNQ7dVnERsFpxN5srztInh/PC73Hdif87hRFJpXSyGYzZDJp+v3RYz8r9thWeXSfVVx4poIgfANoB0Hw41Nv/SPgBUJLvgz81yt2l4FfB/5BtN0zwN++wv4IgvB3BEF4K/r5Oxed7yn8NrBLWFOyCkr0/t+84nEvxBul13i78zMgFMgfDicrFUi1Wsb3fNxLlPGd55Wo0QP8ccoAY2u6lg0FVfE36sw/DBXIo9E0EaoA8ipqlV8whqMTZba3f0StVuHZOzfp9UPlW19fIxUJWdtxUFUZLfqJsVgdtKhUYhweNRlPpkB4jZ1On9lsliTGpVOhuvncYjAckU7pKIrC3v4RlmWzt3+E67ocH7dxXY9CPkvjqJXs1x+MqFRKvPHyFrYbduTrmookhr0jvu+TzWbQVIViIYsiS4iiiB1Z+ZZpkQvAzGUplQoUCjk0Ley0d103+X5mxjz5zPVahVRKR5ZE1tdLjNQ7tFo9Hj46PHuzRYV0Wl+i0HFdl0I+S2WtRLVaXuqBeFJ8nKbby2J7q85a5Wxj5rkIgiTceV7FYuzRLRpRuzc28Vzv3PtynlEILBkLTxMuo+a+BvxHgiA8Av4I+LogCH8YBMFxEMIC/lfgjRX7HgLvBEHwURAELvB/Aq8DXHJ/giD4gyAIvhT9/MEVr+/vctbjOI0c8PeueNxLwVPD0E8tm2Inm+bD4WSl0DJMk+NW58LjCaLwC0tUa+pyabGoy0vexzRSXObal7kh7/9CzuFxWKyl39qs0WiEwvilF5+l2erQ7Q3xPA/LtBBEkU6nj7Nwr5zU1rJyXWjNDvwA3/PDEFgQ5hxkWcaYG7z04rMMo1zDYtWRYZi4nkc2k8aybTzPY/fGJm6UO0ppWhJSG4wmKIrM3v5R2Ajq+/T6w7A/A4feLAyfmZbN1uY6c9PCdZyQ00yUGI2nFPJZJElCluXkOjK+lyg7SQwV0PZWna2t0FMUokq0+NxLhRyVtRKKrDMPVDRNIZtJMxpNmIxnlIsFJuMZrhcm64fDMf3+kE6nT683otXucnBwzN7+0dK9jdHrDpLkd6vVC/frDpLvYjSacNhoMhpNmE5nBEHAZDxjNJrQ7w/Z2z/CMEwMw0xCPrZt4zgujUYr2f8yePjokMFgHFWvnXxvw+F4KZk9Gc9otRaEtyCwvVXHcc72scTXvLd/xN7+0ZlogBb14JyH+DlaOl4QrAyXPg24MLsaBME/IvQSEAThN4F/EATBfyoIwkYQBMdCaD78J8DPV+z+I6AkCEI1CIIO8HXgrehYl9n/4+KytXGfWA1dzBEFsK7W2Gscsbu1iabK3CoWeDgcLcXv4/6BXm91Y9FpxAnsuJEJAFFKkuWLDW1XgaapZ15Tt9L4pouoy9SyqaQMWAxWlyJKZhdP//gW6SJW5Ys810MUheR8QgXSpT8Y8UFzztdevQOAE4Bjw1c/t8EE+IsfPuArL9YRRBHPdekPRqzX1hAlEQEBURKp16tYpsWHD/cTj+P0/VwUGnPTIqVpuFFYqpDPIooihXyOxnELURTOWLBxWLPfH/LSVpqfNxxefr2e5KxEUWRjo8Z0NqfT6ZJO6ck5xN6TZBh0zRxaVNoa7zMaTfA8j3K5yPp6hX5/SH8wRIrehzBcFB+vcdRKntekqbQDlejZOi+ctOjFxbAdZ2UCfLGowzDmFPLZlTmzXC6bhIF8z8dxTpLtCGGl3Hl9IIvY2z/pJTlz3laYp8lmM9i2jZ7SyOXPJrLHkymlUniPDhtNHMelVi2jKPKFTYPnoVjM0zhq4boOxUKRQuFsaPBp6vP4OAG2fy4Iws+AnwFrwH8LIAjClwRB+KcAQRB4hCGr70TbCsD//Lj9P2EcXbzJlba7EPFCbLV60BNopU/CFZIYsJNNL1UjxYJl98bmGcvkcSiXi0mZYLvdeeIqqxhLtfzRws5+rsz0p2FY6EF/vDLvIs8O0Dvfp9Ef0PGzyLODj3Uep3GzdOI4uq5LKpVC07WQzkNVOWw0I69hjQ+acwoZnb/4yYOVP7Ks8v17TTzXZTiaYDv2GeG4t3+Eadm88NztxHo+L9m6e2OTSrmIHwQ4bqhQy+UixWIeIeaBEgQmkxntdi+xmuP8RyadQtPCiOpwOMa2HSaTGb7v0+kOmBtz1mtrmJZF46iF6BmkFI+U4iGk06zpKooUNuvN56GHUSjklry0uWnheT62c1IQ4S2UXysL33tcgSZL0O0NHpuHiL2fxQT5eZVTi9b4xkYNokT56QKPWHEMh2NEScT1vMTa39pcxw/8c4tVFivUVuWIkvPWlLAyzjvhSVtEXH6/+DnbW3VEUaTb62OZ1hMpjhhbm+vs3theqTjg6cp5XEniBEHwXeC70d9fP2ebt4D/bOH/PwW+sGK7lft/wvh9Qm/ncaGrCfBPPqkPDBuFMuRzGdbXK2iOws8a7/H5rRcAkhi86/nIpxbnafbWvf0jqmvllQy8EAr802XDT+J1QFiLHyftbpZyiYJT1lY3Neqd7+NLKe5yizvVHbYIGwhlswFSYeU+ix7XReW8uiSyfWqBybLMrZvbjEaTsJHOtBBEgYNGk63NGl979Q5/8ZMH0bYqrmvz7/07X6DR6vOz+4e4rs033nyF2TQUEIYxD0NUkkQmk+LouMULz93GcRzGk2kiJCaTGXv7RxTyWeSon0Nc+D6MSHBPZ3NEQUjoPHw/7OOwLZuZMUdVFXrdAaZlExCQy2bIZtN8+XmFH7w/4tUbWpK/ch0HVVHwPQ9VUUMyRpbZkvcHYfOgoshJ0cRpbG2un/FGF8N355Vcx6GUxzWuAqRS2pK3fVm0O71zG02nM4NiMX9GuJ9Xufbw0WFC9tjvD5eIHxdh2zaqqlKvVXAch0F3EjaLLpTQTqbGSvJKgN0bq72Zq6LZ7Jy5dtt2PpUc0CeJp0fNPRm+DewB57U7O9H7f/KkH7Dovk8mMzY31ikUciiRkigFJebp+dI+O9k0j0bTlcdbXNi7NzbpD4YcLiRbT2NnhTdwFQ8mRmy5noaypiWJ8wf9Ma7nsy8/w37qFezyK0sKwTBMPLVIekEg3Snnk59YYfT7Q24Xz9fnt4u5M4ojhhuFmzY2agiiQDaTpljIcdzsMJ6dvYb/5633qSwca2pY9Hph5UsqpSNJIoVinqPjFvX1kP5b0zVM0woLGVwXVdPY3KiRyaRRJBFFCkkEO90+tm0nxQaDwZB0WkfTNTRdw/cDJFGkslaiXCqQz2XRUjrZbJrADwiCgFlExw4kISgIy3TnVmhImJZFp9NPnjXLtHjH1smV15lEpdqPY1F+nEHxuJkvFymOGJZtX7nJT3kMCeJ5lYXnXYeiyIkCWCX4LdPCMExUVSWd1jHmJwba6d6L7Dlei+/7S9fYOGotFZFcBrEXtTiaIUZcufY04ek626vimyOfbxa+DvwZYVXVosSaECqOr1+pUfAUfD9ILLvpdMa97ge8/szrfNB9iBDRO+TIca/54My+95ptytkye5093rj1WvL68XEbQRCo16sXkimuWtwxod9VcDqpGHNTyQWN6Tv9hK7k0WjKnfLq8EQqpeHkX+Bm/6fc5RYA7ek8qeAq62H8ulwu0usOuLNWShTKzUL2jCd23nnO5yfK2DQtJEkik07x4/uHfP3LL/NnP3g3eX+rmOawFTbOfe3VO3z3rff4G2/cZjKZhR7GeMJmvcZapYzjeImAcByXg0aT3RubyJwMNfKjbu3trTqyJDEYjhL+Jj/wox6I5XM2DJO5aSWCajye4gdBQhkPUEiL/OD9ES/UBQr5LI7j4XounU6fVKTMqtUyuWwaTdcYGT7FjIijhnmC82L8F2GVQHZd99LzYHzPx/N8trfWGI0m2I4bDryKQnaxx+w6DtpCn4kkS0mIKPCDkMwRECWJIPBRZAX9ks19p/mzTsNxXebmiWcd5zKCBYXrez5HzTaFXO4MAWPMIrCYu1rV+3Fek2S/PySfz55RDum0zt7+Edl0ClGSkvN6WvCrrTwAvjnq8M3CK4TluH+Pkw7zfwL8ycdRHHE3KYTCZmOjRlVY5+2P3r70MSbzMO77w4fvJApkY6N2blkuLA8jWjWU6UlisnHJsO/7yIpMMZNiHDHBpp7Ng+cvMe2uQj6fDS1qSeNOIfQ0xrZDLSIQLy8srMpaiU6nz50nqL+H8H4rsoIxn6MoMuVSkTe/sI0UhNac64Yx/gfNQbJP6+iArZzP3Xv3URQFx3F46cVnw5JcY44sSezsbNA4aqHr2lIoptPp4/sBASElSnWtHCrZXIbBYISqqeRyGVzXpdcfhuErSUyEqarIpHUtoqAZs7O5HiqqIJwM+LVXn+HHPxawLLAseOZWWIGUy2UZDsfs7GxwcHjM2FZ451GTelGm2ewQBMEn2qWtp3L4sszujU0UWcNxQ28n/lvTM1jmDD0V2mHmfMLujU0EOUNhIVqpqDqSpGBGz/fxcTvxCvRUDnM+WfodHwvCEI6qKmEoR84QuLMz3FJx5/h8bpJK6WcYpEejCdOZkVDUby5QvhSL+XDoVbTGYmVRq5ZRVZXpdIYiS4ni0zT13FDWImzbxve9iGbm5Fwft+/ujU18z2f/8JhSKY8fPD0J81995QFECuJfRz+fGFZ5BYeNxtL/v/7irwPwvXvfW3p9vbBOa9SimqvSmZwt090/PD7X8ouHER23Opfqfr0sRFFAlGT0U6FXpaIx+Wmf3OsXV1IdHbfZ2noBtftj7qx98bHbVqvlhBLkquj1wwFHt25uh0nmIGA0COd6xJ6FYZj8vOGwm51w57lnE69id2dzyWOLFUWc0A6CYIlGo9nqYjsuW5s1xuMpqqbheR7VajlJ1O6ulUIPw5hjWSehLFmS6PYGqKqCMW9TLhWRJCnZT5JE8DxazSMOD7f4whtTyuksvuczmU4pl4usVcIY/gdtAXD5+qvriSBdVfV02mu47DRKRdVpj2wO5q/Sf8/hKy+k2W+LHPVtyjmBV2/pjI0ATYR392bcXNf54X2fv/ZiDtMO8AOdDxomr99See/Qpj0yeP12lgfHc3aqOzTHPr2JC0x5pq7z7oMpn98W8fyAn3wUlggDvPpMEcPy+KAJt+oua9k0/X4HSToRV+1Oj2o0G2M2M5ibFqIg4rgO5XKRQiG3pEyGwzGj8Zh0OhMqBsD1PCbjGf3hgGKhwDBK4C/eu8NGk62Ni9euE4XfAAAgAElEQVRYs9WlXquAIFyZEiWeDQMn3GtPA56eM31KsL21dea1tz58i63yakXgBqsTnReFDBrHq1l1Pw7icIssyxwft5N8hqjLCOrJo/K4ZHfcY2BXXkft//TCz6zVKk+UowmHZoX9HKoiE/g+hUIeXQsTuP3BKCHhs71QoGbSKVRVoXHU4rDRZG//iOlsjq5rSeK82erieT6mFcbJp7M5pUKOer2GLMuUy0UURUmEg+/76JqGZVpIopDkN2RZRhJFVFVhrVJCibqw02kdXVPJplPs3tgMk+aZNOv1TV579RHba2n+4t37/MW7Her1cAzu//fzNt95O/Q23vz82oXsrtVTA4dG42nSm7C3f3QuFch4DtWcjOX4vPJMhrfuTxkZHs9upnC9sBfmZ4+mSJLKUd/m39wbc2s9vA+FjMRPPjL40h2dqS3y8m6GN57L8t6hwRfvZOkMHVrDk3DqR81Q6b310EsKDF6/FYY1RZHkWLWCijGfks1maLY6pFIaqZTG7o1NZjMDRZHJZNLU19fI5TOUy8Wo+izs4o9RLObZvbGN6zgcNzs4josoCOTyGXw/rDTbvbGZFCwAyf7x6Nk4zLbY4xL3iCiSRCPq7L+K4oiVfzaTpnHU4iliJ0E4r4LhGmfx6p1s8Od/9M3HbvPDh++EOY/jD5Kcx+NQzpY57h+Tze6wnk0n1O2wTPW+OPmv0x2cW1HycbAqQRoriunP+6jVVDKedjFRfhqx5St6BtJ0H6fwwmM/9zJ01zHuP3iUhJsOG02ymTSe6+F4HqXI0tR0jWazgyCIHAw8JobFX399JzlGXO0yHI5xXA/btpcUseu6HBw2yWYziaB1PTcJgcDJmN844VzIZ5fyB81WN5wNHvi4nocsSWHTH+C4DoqsLIUXG41WKHQEjXcenqWy+OoLRebGHEEUHhsG2ds/RNN0stlMQjnfbHXJ57KYphlZ6AJ+EKCq6srnSO3/FLv8yuO+hgSKquPYqxPHV+k5MoyQ0XdV2GYynpHLZ9jbP2StUn7sCNirIB4Ju+ipnZ63MxnPmM5mZ16LPRzf87Fs+7GjFi6DTqePMTf52//w9/nJh7Onouzq346w1aeEZqsb5i2CgJfqZ4cExfjh4B3eKJ0kyNezoaX4oD8mtyCUUyktEazZTDqMG5cKSwt+VdnfJ4WDg2Pu7GzwoD8m+7lyWHUVKY8H/fG5CiReiL6URtDWkGcHuJmdldtCaNlddk57sZBPqEoEQYioPNLYjovtuImCLZVLaKqM4zYZGafYaRbi0bqmUiktlxaHXE1SxIBrMRhNErLDmTGnUi7S7fYRBAHLdqL8hsRkMkPXNYbDMT/bG/GNN1/ho70mggyL/cqCrOECjdZCxZ2cYe5ArZpHpIu/wKjz11+pIUoi/cEQWZaXPDXf95MwiSiGc09c18W2LHpzEyfqeO90+2gRBQqCQFbXkCSRyXhGOqMxmRjIshQm9cXL0csDKxWHZVp0egMq5fMNnMAPlkKz6bR+brw/buL7pEplY8QholWcZYqskMtnkp9V5xMrtYsUx2Wq1uL82mVnvn8WcK08PiFcZkJgjC/kXzyjQAAKvrcklOPwUXzc+no1qXWP4/fZXObcKo+Pi9N8PKmbWYbfPab4mxfPro6VgaevIZndCxXI9lb9UhU+1Wo5GbazlnRBS9hTg2w2veTBHDaajCwZcLFsl1mUQwh8H8u0lrq2NSlkx53PTXRNpVgI39N0jXo0PxxCAknLDiuHRuMpiiJTrZRoRvxEkiTiOC7fePMV/tX/e3HY7jTu7rUoZHRGsxminOG154scNJoJ5Xo2oybnfdhoktLCMI7jeKTTCoVCnk63HzYgRopsOjMQBQFVkROvpd8fomsqnh9gWS7TmUEhl+Ow0eSZi5nDz6DRCLvpPd9ns1471xCIBzQJovDLH44UPSqLz4woSpdKjkOoRC6TT4oVR7PVxTQtNFVBjgaWjadTatUKrusxn8+fqibBp+dMP8OYzubnNmmtgorKulrjx62/Wno99igOox6Qw0ZzSSHFLKSHjSb19TXq62vJUKdPAs6KCWiHi/0okoi2k1nq+zgP21v1hIdrYEeMoebjewFiYXLeDOgYG+tVRqNJGJ5q93AcFyfq/+h0Bxw2mrTbYVy7oLl88fkaP3yvh207YU7D9xNhDzCdGtHMcjsZ7GQYJo2jFnv7R1GppkKxmEeSRIyZgecHiYCcGfOEmbaQz5HNZvhoL0y+p3SN3/zSC3zjzTAM9PlnT6znO/UTy/w3v/RC8nu00K9Szpx8t1uby8bJ9ladyloJz/NRlLBp0nGcZMrf3v4RgiDgRZavMTcZDsf0ugNUTUOUJDrdPq12F0kU6Q9HITus43B83F6ZjD8PW1vrbGyESiMWlotd3x8LMSv1Qoj9vK5/y7Rot3uMRhOGwzEHB8f0uoOlTvj4us7LGV6FHl7TT6hpLkJ9fY2bu1thVWa1TC6fYWtzPWHgvazS+qzgWnk8ASzTYm6YzA0zrNPOpC5lRcXVPKIkspvZ4uW15/jh4J3kYW4ctbhTzlNN6TzojzFTYQgmpiGJF2Vspa+a8PZx0F0htM1TCyN1O4/TNXFH4Tk/ToHEXFvZTBo3s4Onr6F3vn/heZTLxWWyugVYpoUoiTSOwntZyGcZjydY0djb6lqJdEonrevcurmNJIoUMyLb1QzG/BkIAhRZWfq+KmulxJpfr60hyRKu55JJp8hl06RSepJ7iruAq2ulsMNdEJamMHqeh66plCJald964wW++9Z7dMcTvvHmK+wfdXnzi88BUKoU2Vwr8OYXn+ODR8fJ7197+RZ/443bAHzn7SaapnLr5ja93ujcZsBSsUC1WsYw5kleZvfGJoVCDl3TWCsXqVZKpHSNylqJTEpnNjMQRYFUSkcQRdIpPRl3LMtyMlhqOBxfmpBwEbs3Ns8I+ScaCysICKKAMjsh5Dyvo13TNWzHoVDIUSzm2dnZoLJWWkpix57CqnL40/Tw/YjAchGT8QmxYq87QJZEZjODg8Pjx5bY/6rhWnlcEY2jNpqukUrrtLv9S8XpY5ze1regTImfzu+GJYGRJaSpMnfKeXYKGR70xzwaG0sDmSBcmJ9U4jDGaUusPZ2v3C7/5RrmwckCWqVA4u7ZYjHPUbPNZDJjPJ5iVr+C3vn+hV7I+nplpeU6WOBDmkxmeK6HJEmUSwVsx2UymTE3LRzPSzyYXndA53CDZ25NqNeraJrCd95u8p23m3zvr06693dvbKIoMoV8js16DVlRKJeLpNJpLNPCMi1SupYI8FihLE5hnM4MHMdhNupRikYHf+PNV1iL/v71Lz5PPpPiG2++wnolz+sv3SSfSSW/t9bL/OjdhwDUi2FU2bJMDhtN1tcr6PoKL1MQGI1DSzudTqFrGgeHx7TbvYgry8OLhNp0Gs4BCXsSQvqUUiGXCMhUSkdWlKU53KZloyrKEymQJ5mj8WlhvuBZTWczHMc98TqCgIODY8rl4lIDLZzkPPb2j5K/M5k0O9sbly78+FXAdc7jCgiCgOraiWt5lZhtpzsgH3UHxzBNkzvlm0zdNe5yn21OUTxLEnfKeQ4Oj7HSGQ5HU7bzmSThm8+vpvC4LA4bTTxvmWxuMbk3tk8ESKHmMGqfWNjZF4tYjRlSVkYuaDzoj7ldzIW08a67FErbrIcJ32ari+QKOLkvougKeuf7uOmtc3Mhuzc2kzxI/Nuy7GQ2RrfTp1QugOuF3FGKjG3ZYV4oCE44p4w5Yu5DvvN2VCGFAyjUizKzucV33m6iygK//oVQeTZbHVzPw/cDppNp2BUdBFF5sITrebSnJgVZIAgCRuMppmWH153SE8/phZ0c//e/CZkFCmmRL71wEnY6Pm6HzW3j6TI3WUbkuXrIYJyVw77r3RvbiQWvr2A/1jU1mlYnousquq4mo2ZVRSaV0ul0wxG5hXwOwzCRROGk4zsICfks26ZSLjLvuqQi8snAD6itlel0B6SfsKIorrp6YO4tvX5H373agRZ6IGzbDmfY+wGyLCUeYRD4K7vET8NxTp7RxWINP6bqF4Qk52cYZsIvFzMIKIr8iedsHu01Lt7oM4Rr5XEF+EHoslbWzi7g82CZFoqiUF0rMZnMlpRHnJTNylnKkzI/5B2+lH/lTGXGzvYGk8mMluPxYDDhdjGk+76K1xOj2eoS+H4Sn44RC+eYkuPR4MTCL9Sc5HeiQCQRbSvD8LvHiCmJzMslPiTcZyebXnqw4uvp9wcMhAGlYhHDnPPMza8AoHZ/DKK8sjx0a6PGrD9bKbjmlokyW64MMi0bL6LMAKjXKownM0bdsOP8zpqLJIUJy8D3ycoC6zmf/aHEd95u8uXnC0iSxMZGDdd16XYHyLKEqmloqoIRODRmD5k6xyEzWuT8TR7GRQQmPIot9LDk9uWtZWqLuNhhMByzXltLqE9i7N7YZDKZoSoyD7ph6Mjzw1GtvcFoqdpuOpuTTusMR2NkSSKIaE9URWY+N5nODDbrteS34/loqsxho0k6pWPMQ84nQRRRJYnJdEaWkCBw98Ym06gQoVYt0+706XQHSGLYCBf39FyEYjEfVoh93NTcQjWWqp6/Btud/kqa9UUlsZiYXlwHp0cTtNu9sAItUk6yLIfzT6azJ1p/j8PN3bM9Yp9lXCuPKyKm1fD94FwakF604E/X5OdyJ9UZ4/F0yXPQJY3Xs5/nrfFPybQyvPzCc0vHzOUyqKbFgWHRGU+f+ME9bzraaSvKPaf/J5uzmE4WFGBUeTX87jGpZ/No9RQHUWhkJ60xnsyolIsIgsBLLz6beDbxAKB8LgtRJ7oyeg/JHmJWv5IsdGsShhY832dv/whVkaO5FWGoLPacGkctgiCkD4krk3zfR5SkJCSjygKLkdqNjVrifT2zBrbgctd+j6yi4ds+veGImRYpUTv8yaJRT2/ALCzPfd9/wJ3gFtwEXdcxTZNMNkuz02d3qxZ1Np8UQEBUTOC69PsjVFWmcdQ+0xGey2V4+OiQQlplOPMpFXKIkkhxoWt6b/+InShBndZ1SqUCjeMWvh+EXpjjhiGsRjNsqOwOQgp4X0ORFRRZAUxmMyMZQ2zZNlklFKKLbLWCKGBa1pIXSBBcepZ9sZinPx7iqyfPleWfhI1U1E8s5LM488NxXMy5hR/4S/cuNmgWFcrxcRvfD+h1B1Si616rlJib4XAqy7JwHBfbcShFObLF7+DjwnFcgqeoTfBaeVwBYlS5UqmUksW2CpXHNPBpetiNfDrktL1Vp9nq8lL2ee6uv8/de/eTYUSL++q9AeNUGu0Ul8/HRazUdm9sLuUwQobc0PPw5j51pco4bdE2litxFpWImJLIf7nGgWGBJKM5Dp1Gk93dLWRZDvMSczPpT4mbtabaLbSChmR2uVl0GPXDvIqgS1hT+8SLWMh76FqoyFx3efJbnJcwFkaxptRQOHmejywFZ6pqClqWkpDH931s0yWtq7ycD8upm61uOEDIsnCGHq7r4noeG2Kd8vaJgTAajdF1l/RCs2dKjzvQRQRRTLjJbCes/jqdBFcUhePjNrdubiPuH/Hj99t89YXwM0zTYjgcY1oWsiSF/R/9IYIoMByN2dneCIVgpPzDAVc1ms02guCSUdJ4foBt25iWxVqliKqqWLZNLpsJGaHHBtbQRhTAMm16/dHSTPNKucDB4XHSk3DZ8M0z+RtLoasD+ySH8oyys9RUe1lKlVUQF5RQPLd+OByfGSTV7w+XjLA4n7RIUOj5Po7jUiymEYWzRJK7NzYv3aP0OPiej6LIl2os/qzgOmF+BQhCaKkYrptUb8RexicBy7LJZtJszTeZ1qd8cP/hmW1inp2JfXXm3PNw2GgmC1WUVHayJ4l4pXxSNpp3QmVVTKV4eTMUGM/XlxdN8Tc3ePm3btL/9iFWYwaeT9uwCEolHo2NsBEyl6FWPfHadqImrYd74TxtT18jkFJoUQNaHJtutnsYUSgmhhnNkY7DWnI071tRZHa2wyE+ghhWLt3ZvIWmPR9+huczW6hMcl2P2WzONOJJcicut/I3Tu6DJJHNpJIJfVtb61QqpTNNXbKiYNt2MhEuPt50aiCIIpZlLnmsnucnjLgPHx1iGCa9/hDbCWeVx9ZyXEbteaHiSut68gyGvSshweJho0mhkCcIAnZvbIbUH1Ec33U9ZlMD0zQplYtoqsJkajCZhJ7scDSh2eoyNeYh1b8QGkK7NzYZT6ZIksjxcRtVVfD9AF3Tlug8LoOysXrWy2louvZElUudTtS8earMWBIlCoVcQi3ium5oAAZB4plato3vhYwDlmmxt3+EoshJhfB5yuzjKI7AD5IwpmGYidJ/GnCtPK6ISqWELklMI+HteGdnHS9iVc346Yfw+LhNrztILLitzXV2zC2Ga8Mlfh6IGpo8H1NavWgXBetl0Gr1kof/QX/MH/7hP+f99+/z8uYmL29u8qN/+TY/+pdvY3xk8a+//af8j//T7yMpKr/3e7/Hy5ubyKLIBz/4AY2f/5wPfvADnq/XsYHyb2+j1VMMv9di/IN20hsSf85Hoyntdi9Z5L1umAu5e+8+Hz3ax9PXSKuHiJEbX8hnqdcq5LLppaqwQj5Ls9nBNC1kWUrmREiiyHRqJEJXk12+/5ci2WybSqWEJEvkcpmEwBBAURU0TQvnPqT0JR6ouJY/rgCbTGZkM6nEyg1HxvapRmW/8RjX7a16mDuICBMrlRM2ZEEQktLY+Friyi1JEpFkKUmQz+dmxMVlYDsuPqGxMRyOGY0mmLaNIIRMBJ7n4Xl+mDfRNOamRb1WoV6roCghAZ8dKd3A99F1Pbkuy7JJ6TqT6QzfP8nPlIuFkA9KlJiMZ6xVQiV6VUbfOIwr2gIbXpU7+i539N2VYZ/TYSwnu4sy3XtsuXe1WsbzfDRdo/eXf5S8HudAFEWGIIhYBGQQhCTfVF9fQ5QkVFVNvHAIPRHHcVfObf+4cFw3+lyRdFq/bBTwM4HrsNUV4Ht+whc0ns0Zzi3SF1hepxfFqk70VQtwY6PGBjV+OHiHkplfUjgpx2IupdjbP0LXNIqFLM12j3pEMpi9oIR3sfJrfb1C4Ad8OAxDQY7j8NoXv8i3vvUtHj16BMB//vf/Pv/LH/wBv/u7v4vj2BwehFUh7733Hn/8x3+cbJPRNL71rW/x7/77fzP8IElMwllWY8b8wzHWwYzsa2XkgsZYVhgbFrdVlUqlyN33HrCzvUkul4nCdq+QH72H1BkiehVMoc5kapCLQiiFQj4JI7RaPQKCpPxSlmUcxyVfyPPDP4ed5wt4Q7h7t8b6+vthh/hogiCE8zPCa3exLIt0Ssd23GiuhoMmaZTLhWj6o5QovHBYVCjss5lU8mwAWLazVO2TTulYtk2vN2A0GhMEAZqmJcppMpkl/SLVapnGUYv53GI8CfMlnpCiupZjPJkiy1As5JhFE/csM9xuMYzZHwySUmLLFJPnR4u65Y15yCVlWuGcizj8omkac2uEnJUoFfPJkDBREqLfIlNjjuv5dHvDJ4r3K23liSuVnOwuTnYXdXgX0QkNEl/JYxdeTPIvuzc2lxTHGTyBhB4Ox09UdtzvD880/83nFu1OLyRiVE9TwTw92uOaGPEKeOWZTPDtf/bfMDctepZDKZelnL44LpvQdt/YXCI4vAyaTodG/5gvrp9M8h2Pp7Rdn4LnLj3Qp6t2ViGcQZFNFvyqHg1pPGazXuXDh/toqoqqqXiZLM9vbDCbDJEVOYnbx/HeR4NJkmS/U87Tns6XSn0XMf15Hykl43RN8l8+UZx2q4Uoirzw/G2Oj9uh4BLhmZs36PeHZL0mqVSGwTj0rhzbRFF1dDGN6YevpVJp5nMDXYli/p7Jo3GGvWkOTXbZKYbXLYoCtVqVuWHgOG7YOT438TwfVZERRQnf99jYqGGZFs12D01Tqa+vhfX92TTlcvGMcDhsNFmvrSWCPf5Omq0ulmVzY3uDVrtLEATY0edqqkoQBFiWnfCYQRhf73T6eHKGdx8OeOPZHLZlJzm1uBw17mcpl4tMZ3OymRSj0STiugqfh0XjYzqbE/g+/cEoLH82Lbq9IYoiMzPmbOpD7PQtprMZ67U1PN9PFGG73SOd1nEcD13XEAWiee3ipSbhXVRC+6QQPAdlch/RGeNLKZzCSwRSKJhHowm+H1w4bMkwTEzTPCPsF4dWeb5PEFxuZk7MOTabGWiaSrc3REBgfb1Cvz/Etp0zvHRf/1vf5J0Pp0+FBrn2PK4AQRSSEaPezMSZTJkG/pLFuQqLVlbgBwlbrmW7yUzz81BXquyrh/zVg3t84c6LQJQEVjUs+yRpfyl+qyBILPW+YdI3zyb9U+acfKXEhw/32dqsEwQBsiIT+AGWOWVvv4GqqqiqupQoXJx7HvNz1UgxNs8m17OfCxWesqYx/Xkfu2GQ/0oVdX2dnbTGZDIjldKpr1f56OE+H9x/yHPP3uLhoym3qtsIQZhIbx0ekZOzjAMBSSxhOy5ukGbiSkiBiCIruJ6LqNxhI/U2r732Gj/+scDnXj5JuItCmlY7DM3EisPz/WhEbXg/x5NZmNSOlGNcuguQSYceYDqlU62WF/oFlsOV9fU1Hj46XCIDjGd/D4djfN8nnSmiSCKD4RglKgk15iayFCrh/mBELspHLQ4EK5eLHBweM5ka+L6P5xbIZFJ0On1sxw3nTCze/+h51TSVyXiGG9Hbx2GvrGbxYDgARNrdPpIkJVVXsizheT52xO+lXbH3o9nsnOFMO43DRpMgCJJc2CrrHcLQX8haEpBO69jFl07eDALU4V0ACouvnwPLtJjNjJXeRfzZo9GEyiWLVAI/OKHt98IwWWx4LB7zMgbfZxXXOY8nxMCyMeZzer2LE+aNoxbTmcFho0kul0nCCRcpjhhvlF7DrJi8+94HwMkMZFPVk5LXeKTl43B41OLg4Jj2dL5Scdit5RkhhUIOSZKYGyaO4/BoMOH27V0QQopyQRB4+Ogw2X6RZdfzo0ZGXeNOOb9yZrlc0Mh+rkz5t7fxpi7D7x5zYFi0HI+j4xaN4xZ6Sk8SmuVSgckkVByLdCGe5+P5PpIkRosyFNymZeF6HgcHcDzPMRotK47RaIIkhiNlt7fqZNMp1Ch0JEkSnufROGpRrZa5dXM7ue9r5WLC/wShcXBa6DiOx3A4pnHUTkJTcf4DQgs+bmIsFvMYc5Mg4twSBBFVU0lH9ChxXmb3xiaOE4bJFi3fsIs8IJtOkUmnGI+nyHJY0iytCClNJuFkvvncpD8cYS0k+OPrkCSZSrmA53nYC5WFlmVjGHMEgSSkdhlY0Uz4nZ2Nx3KXzWZGNOPkpE8jlVptnGl6SAq50mgSBOziS4jOmMZRi0ajReOodYazq9PpMxyOESXpsWGpsBDhcorDdd2lfM2qnpMYpxXH9STBX3HElnUfP7FAH4dYIJ8OV12lHPGN0mv8kHf48MM9bt/epZbWaBsWuqYmx7gojqypKtVqeWWoKuvY1KPS4FgoxPX8o/GE7a068dLaqFfxXI+He4cossx7739IrVqhXC5ypxyOn304HC0pE0EUkv9XeSPaVgYpKzP9eZ/s58qo6+tU0xqKomDZFnfv3Wdrs445M7F9F3MFaZ/nhWWVoRwMhzRtbG5y7x4oWfjBDwBy/MabkyWvKb7OxT4JxwnnNaxqRtN0Dc8Pkv6NyWSGaVoYczOZ65HJpslmUqSi0bPAUrimVqskijBGbFjE7KsxC26r1UOVfd56r0017Yad+lFHeFxlpioyfhBEc8PD8GSYz8nR7YVU7rquJvNHpjMjop0vEwR+MmtkNjPQ3SDsmfFJquIcxyUIfMqlAn4QTvLbqK8tJZGPjtvnPoOLz/njCABXUe5MJtOVA5YuQ3UOqwkQHcdlMplemMeIc5SXraiKy87Pw3n3J57ncT1J8N8SlMvFC0NWMVaVHUrR6MnL4jnhNr1yP+wTiRbjIjvs4+C6YX5kiSV38VwWOm5HozG3b99KHvR44Xxw/yGW59E4arN3cMQLz9/Gsm1EUaTZ6vDe+x8CJx7IeaSJsTdyp5xfKguWCxrZF4v0vx16MwdGWJH0/HPPsVYp0zhqsn1zl63NdYx5mOMQTiU/j47bxJfiuA77+3tkN+4BcPv2fX7jzQmdzjInWTadwjBMJtMZtbUyW5s1HMej2eoyHk+WCCjjRr90Wicd0Z6PxhOq1TK7NzaTsKCSKIzVszE6nT6j8YSHjw6TIgcjItoMqdL9sKrK86lUCrx2O8/I8EmndOrra4lHIYoipWIBQRTRdQ1d1xAEgWIxT6GQI5fLhLPlCdA1ldF4Eoa2PI/xeEo6rZNJp6Lr0VEUGV1VQlLIfCbpk2i3e3Q6A7q9IfP5nFw2zXRqMB5Nkm3gLJPuYaN5JZZaCHMFw+GYTqdPp9OnVqvw8NHhmfLbxykO27ax7fN7sZRIMcfVZOdBlmX29o8SVl7HcQn8ICw6MExGownzeTS5sNVNGAMWGYBjnrfHIX5+niZcK49PAf3+kFWFCaPhZMXW56NYzPNrhVf56fxu8trWJS2iVqRkTrPkQijsO91lJdRsLjP2fvD+fZ579hadaUg//szNHYbDMS+9+OwS6+jde/cByEdVJA/648fW68ckkGU9svAlkfJvbzP87jF4Ph8OJzx8+IjhaIyiKHzve9/j7r37qIrK5z73OVRVpV6vsbGxybvvvkulUuLRo31u3bpFtbpGtb7F7apKMR+ej+eL/OitHzOdzZnO5tx/8Ag/CBhPpvQHI5rtXmj5W+HEPTcqnT1sNGkcLVPCxHmAVdQpJ30z4RKLFcOicI33M00L07TodPvomsbMmFNbK+M4DgeNo3B87QoPNZ3S8VwP1/NQpDAPM4uaInvdQSK0JUmCIEhIEV3HZT43EWItKwgMBmP6/SGzmYnnOklF2yrEwnI+tzAXQlqiIEH1yPQAACAASURBVCBH89kn4xmT8QzP8zHm8yvRs6dSGsVinmq1nHgGt25uJzTze/uHyb1M5s77wRLtepyXuwgx48Ji4+kiYqEeez6KImPM55TLRdJpnUIhR0pXSaU06utrqNGsjriiq9PpJ9/JrxqulccVcJ4QPE3ZfBqZdIrGcevM/o/rRD8PgiiQlTO8+94HlHWVR6PpEtvp6XhyzAa7tbm+kuY8LwpMJjPy+Rz/P3tvGiNJfp75/eLOyPuuu6qP6u7pHg7n5JCUyCUlitauDq8Ar1bWWl6vgV1ZH6QvhixpDXshGTYswBAMeG2soWMlwtKCOqiVdmVJNCmRFElxSM5whsOZPqu7uu7MyvuIzLjDHyIjKuvs6qEocoR5gEZ15RmZFfF//+/7Pu/z+J7PzVv3SGjasVr51WthSSsKPrIixwvH5YvL5LLZuEl8+859qmmd8uSCu3+OIFlMJlgtZpEnF13+w3OMHw6x62NMPcnHPvYx5uZmWFpcQFEU/vpLIdc/m83wsY/9P/zcz/0cT16/zt5encuXL/Erv/Ir3L+/jiiIVOeXefZigc9+9nP83M/9HO95zwv80R/9B1555VUuX1ymUikiCgLlUug/LiAgSSLGaIwiKweeGELoZR15RdTrrQn5IVxot3dq8e54Y3P30I4zosOmk2GmOjRGGMaYmWq44Fi2QyadJJ8LX0uURFRNIzmR5Y+C0NAd8eb+Gntuk2IxT6lcwLIdhqMxqiwxO1OOfT4ix0VJFEhn0pi2jeO4DEdj5uaqOI7D9k6NbjfUxSoW82QyydATRBSxbedYwDsLgiiwsDBDUk/EDnyh02PqQM7km8TK8jz5XC72T4mCuSAK6HroXx8JZ56Wedi2fSwbis736dtPykocxz1WWjvL/a9SKeKe4JMTYTrgvd3wTvB4DAiiAH7A5XwWa8Lv3+4NH8mWUBQFURDp9cNFdP+cpabTcCNzFWPGoLaxBYSN82m6Zq3WiEstWkJDkiS63f6JwUr2PTKZFIsLs9xdCyfaL11aPtXRbCkXNv98z2d2pkzgBzRaHUzT5Mb1K2SzGUrFAjdv3SOva4dKWGd5f0S4UMjEz9Evhz+Hb7T5hX/1P/Dv/t3HMbQEn/jEJ2i1wu+w2wnFB3/qp36Kl7/2NW7ceILPfvZzLC8v8xu/8Rv8n//H/8542OG/+/n/np/4L36Mn/7pn+av/urzXL16FV3X2dqphcOAQUC/PySTSZHLZ5FleTKpLmHZFpIkslBNw3CPpN+lJPfQvSZ5oU3C2kMabqHICv3hiPWH22TSSdqdbhy8I8SyIaJIqVSg0+3FvRPbdtitNZAmsySiIEw8OswzB9QUSURTlUMS49Pw/IBGIzTDUlWFXDZNu90lldTJT3wvEno4/9EfjEhoKru1kCqdSSfPXU6JsuvpPoI4fR4JwpkeNEfPD8MYxRkOgCSpJPTMscb1cBj2jhJ6hvnF5dBgSUuHmUfl6YNBTzcc9BOE6cHQxOS+cLJ8a6fGcGhgGKM4K3EcF9t2GI3MuDxXqzcnpbw2+802tXqTjc1d2u0ujUY7/lmvt86cGu/1ByQaL8WDj28nbat35jweA09fSgWf/b1fAmC3bzCa0lMq4h/j+09r6UiSSDaTPlFG/a3S9b7SeZVUPUUymWa+WkJLaAyN8OI/6fVcz+fhkZ6H32pNeTnojMdjbly/cogKem4EAaOxhTEaUymHAeTa1UuIgngo+zjN+/zYy0XDi55P+9O7FL5vHmGyQ7TrdZYW5ydyHm0uX1xGURT2m20sK/SqSOo6b+w4rJZdKuUynufFje35uSr+qItphqUcSRXJ6TrtXrgQ6eKIlCog2QdsunvWCr4fkNQTqKqCKIox6wtA9EZI7dtI2HhqgZ6yQrsTcvtz+SzplM7W1l5cChNFkaWFWVrtbtikdx1kWY5106IZENOyqFYrfOGNJs9fSqInNPrDEYHvU62WGBrj2D631x8gIMSe5vl8llazw3DidlirNykU8uxOzLQURSapJ1BkBXniaKf07vLQyJFK6odmgiC0m41gOw56QotZaEdthHd267iud2LwOc1yOAogyvBAqt4wRqRSSfq2Rjmr8OcvdyhlFWRJwPUCLs5qlJJwf9/DcX0qeYVG12G+pJId3+Hl9govXEnzyVfCv+WN5SS5lMSXbg3Ip2XeNe8yHBrx9Rs1yaeP8axrNJpdOTrDsrW9x8LczLHejNK7i2SHEvsBAlblvQDI4xof/Gf/htfuG2+LOY93Mo/HQES/cz2fakqnOKlDSxxnkES0zOjf/Gz1kEjfNHZ29+n3h7Ft63nxYuFZjBmDgd0LRQgJGV3nDUTZifcGQCqVCnelE5n4qA8dmTidC4JAMplgNDJCkcfrV9jd22e3tn8oYJwnA4EphtakD9L7fD2cC6nXUcoVMpkUhmFQKha5v76JMRoz9s2JvhW8seNwaUZDkmQazTaZTCquYdf3m9R6Nu2xyMBVSWbnaVsqIz+BJaRouUWc3DUaiacwK+/DrLwPWZKQJDH0Ubcd+oPhoZ20LyVxKs9hVt6Hk7uGJjqsqg+pqB2GQ4PRKDy2xYVZKpVSvDDpSZ3ReIzjuFTLxTCw+T6SGL5XoZBHllVmtCvMzM6TzuaolAsxWUCRRDzPo93psbgwSzabPtSzGE68TgAEBGq1fWaqZaoTKY/BcIQxHtNqhxa+nj2OjaJ2ayHV+KTylaLIh4bcjpY6T7N5heOso2jXHvW/nPTB8Udloq8/MPjq3SE/8J4ws3l+Nc2LV1M0ug6SoiIYWzy5Ev7+5EqKBzULSdV577UMrz0w+IH3FMmnZS7MJLi1NeYH3lPE9wMMY3zo+o0UH86bcUUqAzu79UNlr6XFuThwjMcWBAGJxks4uauYlfcxLr2Xh+7F+HuVhw/PLIF9p+GdzOMx8MxqOvjMx38xpupO4yyKnmVa7DfbqIr6+Lv5cyDKQLTq7Jm7est2Y7l0CHfv16+t4nked9fWD6n4RgNsbxW1epNyMY8sS/h+wO2797lx/cqhwBGZR50H0fOGb7QhGJN+KvQ+yKoSs5kMfhD2a1KpFIYNe/3won32QoLBcESxcFiQr905sHRNaCrpdCqm/2YzKbSExvrDbURRJJdNx6ZNo5GJntAQRIF2uxtqQemJY2qrIROnjyRJOI5LRuhSVTp4qAwzT6IoCo7j0OsPUVUFPaHR7vTQ9QSGMSYgQJZCra50OslLXynz9DM+xYKLbY+RJTFedCRJxPN80kkdSQ61mZLJRGzCtLG5y2ycmY4ZDoboegJREFFVOTa6Sk7KeNf0Te6Ml1EVmXw+h65rBwucdMAQdD0PwzDi82Znt04qqWNZTnyeP4q6GiHa5dfrLQqlPJt9gwVdRX+MIcST1G0TjZfY4sDe4KRz+rQhxGkEfnCuc/WkDKXd7jLnr+El53GTJwek8dgiZ6/zgZ/8+NtmwvydzOMx4PkB6+0+s+nj9NylxblTdw1aQmNhbuZcgaPbPd+ufBpRBuK3Wmfu6hvjw5TBG9evsL65xYOHW+RyWVzHxXUPrDhPolieRvU9itmZMvfur4d018GQG9evcPPWPVYLmZiJ9Wu/8VuIkkq93kJLpBEFES2RZmiMeP0btxAllde/cQvLdnlyfp6g3ea9/8m7KFzMURbhaqXCw1t3QVTo2WG5zbZtcA7mJ4rFPCvL8/EMRfQvl02jKDLz87Ok06mwXKPIuK6LPaFjQjgpHvUkNjZ3SSYTDI0Rg4GBKIp4vh8O27W7h3obkiiQy2WxLBvX8+i4KczK+1i35xHH+yTbXyE9eJO55Ag9oYEoxc6C5VKeTDrMBEvlAlpC43s+ZPCNhw/Y2m0hxzRdIQ4cEBIw0ukkg4FBo9FmPDZpNTsUCzlq+y12duqMR2Ncz4v7E41Wh2azQ6vdY6/eIKFpDHLPcmGxiizLdLs9fO/ACyOyp81mMhTzeXLZg6Bp2w75fPbQeX6USj2Nk/o4MzMl9iYN752xzc45zrdIHDKTTtHt9mk1O7SaHer1FrvubMzaOhY4Jt9B1CeMeifnwWm9G1kOhRfVzjdINF5Ca75MKZ/CKj1/KHAYRwRMW+0Odu4JCP7mxRe/VTh38BAEQRIE4VVBEP5k8vtvCYKwLgjCa5N/z5zyvGVBEP4/QRBuCYJwUxCEC5PbLwqC8GVBEO4JgvC7giCc357v2wRJFFAlCWnqglhr92kZE/E4Tpdov333flwiOmvCtnfeEtERvFh4lm65Gx/TtBPgabBMi2qlzMpSeFKHg2MyohTqFJ3Eo59Nn1+X6/oTV5idrcS73xvXr3Dz9hrCeMzlfIZ/8k9+jN/+7d9hY3OTm2++yWc+9wV+6Zd+iZs372CaJr/5m78FQDabx3Fsdnd3+df/+l/z+c9/HllR+MxnPsNC5SKW45OQ0mFGeGmFlpOdWM0eqOECaIk0WiIsh1SqVRxPJZvNk0hoyLLMzOw8nu+TyaTC6XZNZWVpnqWFWTRNpVgIfSz6/WE4eT82yWUzCKKIYYzx/IB6vUWr2UFLaDSabZaW5tA0Fd8PJdc9z6dtJ6kpN+gkr/OwK4M1INd7mYqzRjqpU9tv0e0NDu2GP/XKLrYbcHsnZNfV6y3yuWzMDJtMRiLLMpVygUqlSDKpUyoXUDUNUQw1lUZjk0qpQK8/pNnuICDETW1JkpAlEd8HwRtTqYRlnb1ag26vh57QuHhhEds26Q8G9AeHz7HH1axSJrI3cFAi6nb7rCzNUZloxo09PwwgZ1RIopmW3KT5X8kElMoFZmZKFOcuMOifEhSmruNcLhM6BnI2e3I8thiNzNPVhIMAxdjALoTlTqv8Ap3++NB7jccWgiAyGpmx9exMtUyvNwTh8Wa/vp04d9lKEIT/FngByAZB8EOCIPwW8CdBEPzBI573WeB/CYLgU4IgpAE/CIKRIAi/B/xhEAQfFwTh/wa+HgTBv/lmPsy3Gs9cSgd/+n/9PA+bfeZSOnvGmOvXw7ru0HZIyjKiKFCrN4859vmeT32/eaaE9f5+i3KpEMp8R57M50yXIbzwNps7ZEuLh5wATxva080xC/MzoefEyDy0M6tNWD+u61Is5NAS2oEX9ZRf+eMgck/c2tpjMBzyrhvXGHlhKUTwXNSEjuh7iHKYmfj+5CL2A4YjA11TGbsCuipT6/XQVY1yNsPdXZPu0GEln8YkbDYbpkclp/D1BwZXFnQKiTG3axIzeZXNhsViUaIx8FnJp5mpCnz61S5XFg4yyqQmMrJ8DNPjxnKSBw/uszA/E/89ohJJo9HGdV08P7S+LRZyOI4T60yFyrweo/EYURRDq9+tPWRZIpPNxCKGkYz6TDFBt75FIRsG6fHYQJBTNLoGA1th20jxwaXuoWa+k7nCl3YKPH8xHPDzgpDh5/supmmjKArNZpeZmQKeFzA2+mz3dYamz7MrIo4XSpXUDZ3L1YnPuzPAV8JsI3LOrJQLsQrvSajVGseE/k5rjB/FdMlp+jyLkMWn+ojSUgTRauNrR+VivjWCjEeRaLyEWQntlX3Pp9cfUCic7mEy6Bu0u734O/qeH/tFXvu7VLYSBGER+EHg1x/nxQVBuAHIQRB8CiAIguEkcAjA9wJR4PkY8COP89rfFogwTEJuIU0z6bOUS+NPSllpVeHBhFGkSMd3D6IkMjtTOXb7NKrVEoIgMBpb8e7yvAv0V9ZfhaTE8vIy+ZRIOS3F/7q2Qdc24t+Hw5DiG0yMcEKWzmSYLwiQ9u6y/OYnWH3l17n26m9S+urv0Lv5FXq9AYEfsFrMsjcc0X1MjnrENBNEgQsrS7xx8w4J0We73Wa92+NOrcat/QZv7u5imUMc28SxTcamwcbGFn7go0ketb1drG6H+nDAm7u7bNZNnr2YpudaWG7AZsPCcgJub4+5PKuwUtX4whtNbiyGgaOUkdlue6QTEnrKZH0vDDArVY2VqoZhetzeHrPZsBiaHl+7vcfdms3nX69z82FIERaEMIAoE1vcyA632+vHpbKEpjIYjmLDqnwuE9OCvSnhRHuiPmzZNlv1ASOpxL6ZZiRX6QYVBkGG+YtPsG2EO2O18gRO7ho15QZr9gXk/Arvv57hr+/YtAx4YzMMoN64TTKZ5HNvGKy1Nb6+bqEoCtl8id2WzXuupvnLN22+vulTN3T2ey7tdvdQ9uxPdMNmZkqHjvkkTFNgt3dqk57R+c5fQRBoNEIGUqQmPN2/6yMe87Y59bX845mDIJyd8R/Fo2Zb9vb2YxLBtL6bmzowEPN8n0IhdyyTmX7t6cABbydB9nNmHoIg/AHwvwIZ4GenMo/3AxbwF8AvBEFgHXnejwD/nNAB+iLwaeAXgALwUhAEq5PHLQF/FgTBu054758EfnLy668GQfCrb+Fz/o3g6Uup4Pd+9WepTkT+Hry5hT+XO7FJ3TvFJvY0yt9JOzTf8xmZ5iMl3Dfau9R79TMfcxRXF64yHAcsTobSbt+5z/ULs2T++mOIRuvYSRwAlpbj1rv/MRdmwh3iwHapD0fnpt5OwzLD6eT6fjM0YFJkDO3wjlYWBC4UMrElb7Q79T2fRrOD7dikCgXaU1POG19tUS0q2E2T3HuPZ3lFRWRts4dhHV4IU5pIWhxx7do17ty5A4TUZVVVUBUlpGK6HvsNi/2xg4+CKgt88N0zbGzusrw4hyAKcYN4a3sPRZZJpZKoisxurUFCU7Edl4X5KoYxjqVLomE207Lj88Mam+QmTfhsJo3n+wwGQ159aPKhp6s0m20KuQzd3pDllRUg9GJptxrMzM7jOA7dEeSTYRbiWCMULYlffwVsA6/6ApKiIkkKvuchShKB79PvtdESGqJr4MupY83ks/TYpjPuvb39mJJ8noZ5hHa7SyGfY3N7D1WRKVVKbE6VnTKO88jeodW4h63NnyhIGGU1ETY2dxFFAUEQkESRubkqg75BKhWSB9TJ5uAsTCtaR99bJMcenbN7e/vxRL9pWuiJ0PhpbI5JaHr8mf5OSbILgvBDwH4QBK8IgvDhqbv+JVADVOBXgZ8H/qcTXv+DwLPAJvC7wD8D/sMJb3ViFJsEi29bwJiGIAroU30AM6FzY2rhHDsu+iQ1TqePX3gQlm5OYnaclNqLkkir1X1k8JCPZDoZPcOV+SsILryy8TWuLlxlJjuD67sEns9La19mr72HolZjLj5BgPq5X0e0+yfufgRAs3o8devf08z+12h6gowqkykelBceJ4hE0va5XBgctGIB3RxTLuZj2rEbBHHgsEwLx3HZ2t5DFERsx0EUwRkalDWFpg+B50NFR7+QRFtI0frzbaSERPZ9VcRE+B21HZ/iXIYVx2E0HscWsKEPxgLgH/OOh3DRGY3GPHl9nicJNwH1lslffK0GiFTKFp1uL14oVUXF9z26vVCgUBRDyXNNU2k02pTLRdrtHqIoMBqbJPUEjuNMSBcukiIzNEYICIzHJsVCjrFp8YF3lfnc1/e5WgloTmRvHq4/wA9CMUPP8+n17+L7PqIo0iFkY2XSKYKgi6qukEiriIGPb5sYnXukgzZ4YwRA9pOIwiLISQZ9A01TD30H0wtvo9mhUi7QanYolQsEU5lJlKU8KtuOFtkI09dGMqnTarRYLuXZHIZ9xYGi4OzUyGUyp6rVpnWVreHo0P3R3NL08Y9G5onXXSabYtA3Dt23tb2HLEnMzVUZjUwMY4TtOARBwOL8DKJnonTewCq/ABB/pqgU5/k+i5MgtLG5e2oA/LumqvvdwH8qCMIPAAkgKwjCbwdB8BOT+y1BEH4T+NkTnrsNvBoEwQMAQRD+CHgf8G+BvCAIchAELrAInF/85tuIac/my8UM7b5BcXKS6lP3SZKIKIq0210URYmHySJJ7fMikrM4Cyn9+ODhTnOHq/MhRfHuzl1msjPIosyDRiheKE9sbMVJb+VdRdAfjM5MmwVAGnVJDbbZ7ufjXWYUNNojk+KjPEVOwPzcDHpCCz1E5qokWh1MPYnb7aKWK6y1+xRUgYSmTnw2QgvZ/f0WY9MiX8ixqsrsdgRWLvTI6To9xpT+/iQwvNHGbVpoS6l4an2gKOREIa6Fp1M6rWaHbC4T18Zd18VzPWzHxXG92HEQwsZ0Li3xkedmqdWbfPl2Ax+F27Vw+E6VBZ5c1MhoGoP+AM/zSaWSsVpvu93FcUMZkHwuM+lRBLGAYahiG6BpapyxyJIc+5Hv9X2uLabJpFPsN9sszJQZDAwkSSKZTFCrN3Fdl1w2FEeMzsNoaLFcChWDGyMVdeHp+HPt7+0zJ6TxH+yQuXIROOhlTC+8waQHAgcyO7bjxsOxkcOl73lwSq8hCjonIR7Oc1wGfYP5pM7uxEbA1JOYrodzEsU2CJDGe7jehcM3n7A39f3Tr8WjgSnaFNi2E4tIRmi3u8yKO3HgOAnTFOKzekBvp8mJRwaPIAj+JWGWwSTz+NkgCH5CEIS5IAj2Jv2LHwHeOOHpXwUKgiBUgiBoEPY5Xg6CIBAE4TPAPwI+DvxXwB//jXyibzGOKuH2uj2K2dSJu++j3H+AUun05tlJeJT+1dAYcXf/LqqkYns2VxeucnfnLtX0DK89fA2AlcoKD+r32WnvslRaAsC0TXQdDC2BHozxbn3hxFrxUQi+i7bxCrPv+bFj972VwAHh9xRlGL3egH5/wHPqQ161SzDJ9Dp2AFoC0XJIJsO/QTKVZGxaZLN5ej2BQtbh9U8voOuw8K6d+PUj8ylp6LL/59toi0mkogrzaXqDEQlJZDGXPvZdRz7XWkIjnUqy1uoxvVSlUzqWaeG5Hh94ao57E3mXQj6HLyZ59eEQCHfMqiwxdMcUdT+2D3Ycl8ULi/iez9ZEn0yWQxZXtHPX9QT94QjTNOMF7CPPzfLmwwGSLDMyw+GzyN2w3x/S6/XxgyAWXmw02uhJHUUSSadCKm9Uepo70uDOZtNs1VvMOU7cEC0cOY+HQ4OhMUYUBKyJdzpwYonnLGVb85T7Innyhfkqw9GYdFLHcz1mZIn6tKrDJHBMZ/hq53XW7AvHJdSD4JiE+1vZ5B+3jQ2PIxiev59yFNPN/LOozd9p+GboB78jCEKFcEP6GvBTAIIgvAD8VBAE/zwIAk8QhJ8F/mISZF4Bfm3y/J8HPi4Iwv8MvAr8xjdxLH9riPoVlushqiIri3Pcr7dYPaMOOxqZeF6oIWVaNum/QeewlK7z4sVnw4Y5YZYBcL+xFj9mo7ER/3+rFTbLdf1g95Mtl9BuP1o2OoJnhBfKNztIOI0b16/EZZFcLsPOwxHXn7hCovESdeZpSOHiMFAU+qaDrrt0uz1WVy8xGo1YW0uRTCp830cDvvaKQFKWDsnHAHhp+VA2Mh6HnurZ91VZ88LgX9RU3AD6JyxsgiSePEej62wMRqgzM6wWw0Co6zZPLepUq6WwSU6Cketzc88n5Kk4gEy5ZNAfDJGk0JQqcpmMEAWFUvEgsGmJNHqQIZu1sR2FTrtLUk8wNkPzq4yexHbc2Ja2UinSbncZWDbpdAp5agNU32/ieh7FQo5Ot48kiqTTKYR6OMcwzYJyHJdOp0c6nSKhqfi+T7U6Fz8mEiRcf7gdlwMdxz1d3faUXbYkiciyRH2/RTGfwxiNGI7G6AkNxbTi6fNIqysKHOPaTTbsCkn95Pc7Sj33zsg8zkLUn5xmQnrugfhh1Bc6yj4bjy2GQ+PYNfO3wQL7VuCxjjoIgs8Cn538/3tPeczLhE3y6PdPAe8+4XEPgBcf5/2/k6DJEo7ln8omaY+s2N88SnG3d2rMVssnPv5RONGydjIZLIsyL1589sTnmZ7Jw959XAJGgsNV4fIxGmTLGJNC5XzOJODISVzPR5r0Yh6HTnwW2t3QHz3Ru019nKF+6x7F4iqzM2VmGi/hoXJLWkWQRB72RyiIjIwho/YudUslJ4lUjBQvvidFt+sxl8+cqugbZSOJC2mMW2EwlHSZvVICJSHFPZLHxVq7jzozQzGt4PZNbt66R6UcMpUuLpd5kvA86Fkyta7LV+6Fx5dLilTMcIYgMt+CcEhxNDYZDIesLIcL8saGwPXrAZ//vMrMTNjcH41NVEVGkkQsy8Z23NhsKvoZbXzsyXDeoG/g+T7JRCLsxRXCRbjV6pAl3NHnMhlGIxNJFPADGE+avUNjRCGfjd0YbdvBdT0cx43nRmzbPtHcKcJp144iKxTn8+zt7dNsh8yv0DNEoVotsb1Tw9STbA7HXM7L8bmXrF5F39vGskVazQ6SLCEIAkEQoOsJOp0+rhsOwj6qCX4WorLToXM+eRAktIQWvr8khXYMfoDr+QQEh/pCb3e8PUPetwnekQny6OS5PFNifXOXi1O1zChwTCOdSiI/5i5je6fG4vzMiZa1jVY3rjufhLXeXVwv3BHJk27Gw/4Wz+SfPPQ4NwhQbnwA/5U/QAzO3o15gkijcI2iJMZlqp5lIwrEBlVvFZcuLJNovMTXeiWKq3lmlfCCvHnrHqJY4Ylrl7neu4Nkd/gGT+DoCTYGIxQ3gYjFU5fmwkYp0PSh2R2QVVWq6cSpk/eCJMaBxDc9rB0Do2cj51TspomUkrG2R2iLSTzDRS0nsLbCxVhbSuGNXZTFcCesZuRYuLExdECUwkCSz1Pb2+PmrXvkslkWF2YJtvYozkkIooie0NhuWXzh5gFFVpbCXevFC4tYpsXuFE1V4CFffM1DzIQ9sbFlkctmMEZjNFlmNDbJTyyEHSfU4EoldRRFpt3pUSoV4tKQqsjIisxwNMb3PRrNTrwTLhbzMaFClqVwiFCW6A3CHs5+o025GHrH702OT1HkWIBxdqZ8pqCgfYqWm5ZQ2N6pUa2U4jJRrd4kkwkD0eJC2GcaKir3u4O4VByIMstqjV3piZixdWJ/IQjY2t6jWn7rWfMxKZQjw32CKFApPr7lwtsJ7wSPx8DReqQoiYxHJnoywfwJInBR+hqxUiKFU1VTyWQFCB49xq7TWQAAIABJREFUra2pKkw48NPp7mBgnBk4brffPHZbNlDpZ+y4vzC9oN4ZJ3kuVSQYNk5tmgeApxcQ56/Fr7GzW8cwRly6tMJau081qb3lIKK2v86rzRLqRZWhN2LNC8ttEftpa3sP09S4nFnlRqKOl17ibs8lly/zQkUgKjQNjXCC3fND18P1ZuidvdUzyKoqmhqEi/sRiAkpbqjDgSR8FFyO3h54PvYgXACd7SHRK3qGGwed5JUst9MjtJkks/kcxVSCjc1tDGOMruusLM2ztVNjLp9kdTEPvofj+Xz5To83J8q3T11MkU6ncByXdrtHPpfmjZ0w0Eh6jkraYb8ZDvLlsmkYhzM8jWYbTQ39SFJJnW5vGOp09fqUi3l8P8C0LCopnW6vhyRJiKKIPm2tHITZTxCI8byC5/mUijla7R7GeExST8R+7BFKk1LSWbaq/im78EHfYHFhltHIjINHIZc51EyenSmzsbmLk07TMy0aIysOImPToigKlIqn9BcFgaXFuWPOhI+DiDK+s1dHVVTmc8BUO2Q0Nhnt1JBEMQ6eoiAgyRKSeBBoQpacP1lbBE6t5X0H4p3g8RiIstTx2EKWJRRZgl7A2AjVch/sN5mrljFtF0UQCcSAzd09rjx3gfWH24fkq33XZ69ep1zMH+LN+54f24RCKOQ2GByvk0onDCJGOClwRMgGKv3ZEyRQclkG7/8vSfzVv0WzuifOeYyUDM53/VP0yVVy89Y9BAGuX1tlfWOHYjb9lgOHZDZZ7yiUMw0qr91FsUY4WpLG4lXWSuEF9YTqomx+CWnTQFACBFHnadfHVNK4l17gr+48xcJTA2qWQzYQkMwxuWyolYUXkJBEcrqCJkmQBpEE9eHjuTlOQ5BEtHxYX9fyx3ex6XcVCTwf41YXf+hy714fbTGJsphGm8niATt7dQzDCIULVZn9/R7lUoHvebqKKIlYtssX3ohMiSwuzaW5e/egOfvKnX0+8txsqK02aQg7jht7lM/OVmg1OzRaHXRNm6gGhAq8fhBQLuXZ2q4xUw0l61VFJjs1n1Qo5EgmE2xPFkLP95GkUFojUtH1fB9VkQ9lElH2cpZVrHiKZ8zINJF7A7q9Xlyqq+23KBVzh/oDK8vz8QYtl9BYa/e5IeksVMKNXCQ38q2CKIkxiUFpvISXOOh7ntfzHA6Xfd9OHubvBI+3gGleul456BT4Ug69qLPT7rNaTNFotLny3AUs0yKXTVMs5tnfb1GtlhAl8ZBkddQsFiURURIPlbfanR7GaIyAQD6XRktop0pEeI8oOwEkA4VX6q/z/My7D2Uf9wwPnvunZDsPubj7VcRhE1EUcVNl7idXmX3ug7Q7PdZ697j41DLyWCIYhE5qvudRqzeo1cPyRZTZnGf2Q+nd5taWw7vqn0W1h0jeZBEatMh29rDUJAKgWmMk/3DGIBEOGrntdf6B/qc8GPwQw0wxbHiLEvhQzOcZ9gcIto2STmKZFklJQVEExqpE335rjdPzYLosNp3VWF2b8WstPNMjeSXL2noHbSYJI4tmK6RTL8zPMhgaPH8pSafbp5DPslYLBfU++p7L/OmfivzDfxgGVssc4jgO9shlNDFxGgyHdLt9XM9HkRVM26ZULtDu9LAdl1w2jet6iGLYn9A0jUwmRafTo0To3bEwH/YGBAT8IEAQBMrFPLX9FqoiMx6bpNMpfATg8UX9osXfMi2iqrAoiLFWVYRMOnliMIg2XsOhEc5gBVNlrnTyzJ7LUebkNGzXQ53cf5SldRLMyvvQGl+OvTkiTA8QnoggOL289h2Od4LHY8D3g1hN86SGW1nXGNoHi9v09H5MJTyB6gchXfUo0yaCJInHtLJOg3QOYTUZgZHqxY3UaayWclB6GmP16VjifGV5nkS3z8sPblNSMjHd19U9EmYCYzRGVhQuXw4nnWv1Jjdv3Ts0IHZiw58w43jY9Hmm/nmkcQ/xSNoueS76OAxwZ7XkZWzEscPFe3/CxlP/OeVykZ29Or4g0fJ8BEmChM6Dc6oCf6uh5VW0Dx9MXg/faOMPXUb3+qSfLaLNJNm3PRib9PsDUqmwbPXCE+F5t7srcu3de/zxH8+hVELf+EtlFUWyURWZRCJBaiKyqCc00ulUrOQqTSi7sizR7Q1I6kk816PWbaDrCWZnynib2+G5GpVqJz+iDc9Ji93W9l4853HexXA0tqjtt9A0ldmZMu12l4WFg03VxuYuekLDcd1YBiTSCJtGOp0iDdAOr7/zXC/n3eV7/qODB0GAUwgFMgxjhKqqodHWZObmpOOJWFtvx8AB7wSPx4IoCseCxnQvIq9rh3by1WqYxkqyFC+eJ81+RDgpcACoisLOblji2m+2H0vu4TQkA4Vb7l2eTj91yONjrd3HbTTQ9TCjmj6xV0sL9PpDNFfGI9yttzu9mPq5tb1HJp1idqaMqshxJvLE1ctx4Ii+n6QsIY5GlP0tZlGRrP6xwBHhvDwukQDFHjAvdHGlMnOzFer7LXKKyHhsYjsuqiLjywrjM8p+3w5MZydW16b35X3UcgKQ0S8XcYBuu02jGWprXbt2jXovww/8fYOxVabZMbm9EwZGEYsX0h7tzoBcNo0gCHQ6PQICLNNClsPpdc/zY3OrdrtLYrKAR4hu7/WHKIp85gzCaGQ+9nnp+z6WbVMpFzFNE9u2SaeTcZCLTMgs2yaV1FEVBcu2UWTlVA8O0TvZcO0ohkMDTdNODQpR1jEamQyHRnwtnwbF2MBJX8B1DzzOIwdEy7IZjy1EAQRRjDeQR2ds3m54J3i8BUQX3c5O/dAuCcIhwcive7WYnTA7aly8sMjW1h5LS4+/8BeLOXrdAbVv0vt8GhH7antrh0S5hDklXe77PoZxOCuJZNUjY6RkdeKiKImxk9rS4hwbm7v0+kMW5qoUi3nu3lvn9t37zM/N0Ov2ma0UY7mVVnuNdvk6y+v/HtE/3sB+K5B8l+DeX0N1FVmW452yntAwLZtgUnqxO714B7ve3wTA08LgtaqFGVRkR3oSavUmlmWS1JOxeRRMWed+E9DyKtpElysKJFJqInvzrhmWcilEPG6t1+nWBiQ0jUuXltHTMsVUAt/z+dzXdyb6W2OW8x5LC7MhGyuhEXR65LIZmq0OlXJxMqk/JpNOs7W9RyqVIkc4o+G6HvmJu6TjnP436g+GZ5dnCOcy9moNJElEmJx/lmWTnElg2zaCIGIYob+JKElok8l63w9ilWKAYiH/yPc6D1rtLnpCIzlhoh26r9lBEAUkSaJaLTHoGwgiiGJI/63VG/EsC4CTvnBIURcOHBCnN2DTTfq300DgSXgneLwFRI3Co4EDwgtutZjF9XzWd+t4CZ35apnAD95S4ICQn18qFyhRoNvtMxgYqIp8qkDdeVHpVWin2zyfWzmUMUXsJtdxuXtvndVLK+zWDsxvctk05o6N53pxIHUm0hTR/fVGGOhSqSQJVWV3r042m4kDx81b95ipLDNutvDsb26xPQrf6bK+t81KdZ7B0MC2HTzPI51Jk07p4fxCNo0xGrNmbcApX+NZF/dpZZHIOtdxXLrdPooi0/wmqP0nBZJWxyZ5JYsoBRRnl5kthHMF9UaTGrC0OM+Hnl6g2eqQTCX58p0ea819Ls2EPQ3bcTFNC1EU6Q+GWJZNLpsln8+iyAdlVUWRGZsW1WqJre09konTF2zLso9pX03jpBJNdL4ApNOhfXL0/K3tPXw/CC15PY/afgtRFJEkkSDwqddbJBIqruMeUgawi8+EGh+PWJgNwzyztJXNZRiNxnFWfVSuJKGpx7Ifu3jY0ujhxg4XVhYm7xdmIbX9FivL8/ieHxJu3sZ4J3i8Bezt7WNOlFyndx8Alu2gKHLo9mY7XJip4DnO8SG6IGB9Y+fE+i2EO1gvCGLXONdxkRUZSZKOBY6zlE7Pgqc4eKrHxsNtmHKE65sW2YSGPHHW6w+GcZCI7Fg3NnfJT5qaW9t7iBNKoq5JOO6BU52mqpi2TS6Xpd/vs7EZZjWZdBrP8SlnC8haFoyTndneCiQlQSPRoDwqHGq6uq6LNVk0gUf6RSuyjDA0EHQBXzq/CRaEC29czoSYVgqQVdUTJ9ghnHCfVgmexnQgCTyfzOfrvLm3w7blkPvgDNevrSKIApZpcftu2HQveB4feW6Wjc1dGgOHBxMhx0tFkwvLoRJwrdag1x+Sz2dxXIfAD0gSUmY1VWE4NMhns/SHQ4ZDI959iwKEKv5BOC8iSwyHBoEfTm9Ha7ggCCee457nszBhJXU6fUzLYmlxjp3dOoIgsDBfCRWGPe/Q7Ei73Q3960fjY3R1X0ogeia+dHZmoijSmY1wRZGPqWJHWlytL30cb/nDx9hiktnATy3FQTSS7Y8+a7c3QJvolr1dp8qn8fbhhX0HYW6uysULi8cCB0BCO9i5eak0siSyW2vE2v/Rv5BrPhtnMUcxsO04cAAx+yozKZEMjVHsTzAdOM6i6R6Fq4+4kblCUz/snbA/OkitNVUlmwkXPUkUKRZyceNSUcLas6aqqIqMZdn4QYBlWaHa6GKW0dhEkiSCICCZTGFNvC0GwyHtfnj8w5mnCKS/GSPJQFLpzbzIi4VnueXejW/f3qnFOlX5fLjLnhY6PAnSYIDYakHjeJN9a6eLZbtY9skMo516+JzAD1jfbLOyPM9qMYtujk8NHABjP+BqKdzNzqTDxSurqlwt5bmQOwg+giQy99FlmEty7YeuYtzq8tpfrvPmrRpbI4tr165RKZcol4vcW3uIYRg8fbnI85eSrJZ9HrRF/vK1On/19TqW7bA86Vd4nh/v5E3LIpNJYZn2JBgEtNq9SRO4QW2/RbvdJZlMIMsy6Yl1bm8wCCfQC9lYbuYoGo3wOzEm1q+lYj4ueyY2PsPiwixb2zV8z483alGm4Hk+49GY0eh4f2M4NAiEsxdmf2LaFQWOR3l3ROgPDVpf+jgQUnE1TTuUPUmjUE9NEsPJ8pXleRYXZllcmGVleZ6F+Rmq5eKZel9vJ7z9w9/fJoKQ0niqVg9Thjh+EIv6FSaL1VHIsnxMTj1CNqGdylCKm/SpJDu79UOU38fF/nYbL+dx89Y9rl9bjev1Uc/m8uWVmHVlOy6O48Q7rkazjaapWJYdS2O4rks2k6bT7WKNpVBKfCI53usPQ6XXXJYgCHAcl436LjMJF11MoHvOiU3z6JZHVYgDQcRL5thbzLM6ue0rnVe5Klw+JGsfeW74vk/RyGGnHYbB6OQXPQULs1k2drpcWCzQao4olQ9nJroa/l2393oAtDtjeoMxF5dn4mOwkscJElktgaIlad9/BTudZuny6uT5XRrNNru7u9w1DN7z4Q/zyT/8QwD++Fb43B/90f+M3//9T/Dcc8/xv/3ar/GP/sW/QJkwf9KpJJIosrtXp1gs8PeeytPp9OgbFmtNkbVmnUtzKRbLWT71lftAGtZPtlS+W7sf//+jL14GDqxbVVVFVRRSKf1MF8HICz0KLJEw5MryPK2JpqWuJ2JzrWlUKsV4wR8ODVrtkLQR0o4FhobC7Ez5VE8dURLZ2NwGRNJJ/dgxuq7LcDg6ds0W8lmyS/84Djq6rpFKhn93tf31uN/h+R6D4ejYcT/c2GFuphyvH2/3DOTte+TfDggcChy+H+D7XjiB6wdIsjRJ1SemQLNV1tp90gMLW3YRJwmCLEsHNrOnaDB3xxb5qXmSSG57MDAOpdqRNWqvPyBIPX5x3c4OqPQqNGYb3LqzRnlpkf6Ebrz7xa+ipdNcnZlqGtseWfXwBDLR71EgHFkkVZ3x5i6DRhNnaQFJEhmPx6RSqbjBrigyophiSIr65fdzYeML+JaJOCVYF0gqXiKD4/skbAPBO3nX5goawyDP7cJ/gyQ8BOAZRO4EIKsK6dTBPE40XTwYjg4tHGvWgYDk3t4+xbkZHuy3kDyfoznmXnPExeUiQ2NMf2RSIsnWTpelhYMFY32zTS6jM7Y9eoNwlxxlJBEzaafROcb8avYHPP/88+Hncm1sJH7v9z/Bz/zMzwAwu7yCKsn8+I//OJ/85Cf5/u//fpLJJL/8y7/Mc889x1NPh7X34Rttuk2L3AdnyCkiA8/ixvUr+J7P7bv3yaTTrF6cR1PCYPqpr9xndeWYDN2ZkNQUzfoec3PVeFMTMZOiPohlOWQyqUNN7iAIGA6NeHYjndQZjsbxzr71pY+TfuqHSadTh4LQaGTyp3/2Sb7/ox+l3e2iaRqapiIKAqqi4Pk+ljnx/hgaDIYGgiBQLOTRdY29vX1sx42HD6fRanYoFvPIshyX8aYRKUTEV1kQYDsO5YSJk78RPy5yhjya0QiCEPc8IhjGCFGUaE00vB7l1vidhHN7mL8DeGY1HXzm47944n31eotKpRiLvUW7nrV2H2U4PBeX2+yYyOkwnrcth2r6vFKFISzTYn209ugHHoHaz+CLAe1Mm2KrAMWwVn9Vltn+xvnLYCehevkSO7aFYRhUyqVYxmJamkIURRbmq6ytPaTiNLnQf53AMrETJZwLLxDMhb4kcuM+7r0vozkGBBqqCvgue/08+7n3M8pfoVwRGebfYFVbQWl8jS/KAu9XyjxoS4f+BhFzbHq+ZmNzF2fGidlWlmnRaHVOnRb2PZ/B0CaXS7BTH+K7LksLedY32wffraayMHNQHms1R5iej23Z5DI6xULoYT5AOMR4i2AL2yykLmJ7MllVpG/7JCSZ+nCAriiosszte22eeaKKORmuTEgyfctkPNnddz+7B3DIFAtgOa2zdv8hxUI+9Efpe6xt7fODf+9pPvnFbyBLIh99/7v4sy+8jjs5th/+0DN86ktvYNouP/iBp/h/v/ANIMxAoknp0Sik3cqyFAcH3/NxPQ9VVQ59r7ECbT2coj+tiR31J4aGRa/X5eKFiwfnjyTR77XR9RSSJDMc9sjmiqw/uM/sTIX9RhvTslhZnj9zaC8a4D0vIqWJ1OBNRtknkWX5ROXcSHI/qScwTSsmzmxs7qKpyiHl3b9TToLv4ACeH2C5HtoUSyJaeI46gw2NMblchrKunVv6OXD9sEntuJSSSSzbObFsdRranR5Xq9e52wvrGJKj4CnH6ZVqP4OdHcSPAZB8iYSboF3q8GLhArtf/CqRM/PFj3wP63/xGbR0mtLT18H22f3qKxSuPkl+qYo/kaMWZQWr36d16w65mSr79x+wf/8BCpBaWmA0NklfTGJuWfi+EOomBQGiKLCzu4+WSKAuPMU94zKCIFIQ9skKHe6v3ybQCgRBirn3/hhKIsUXv6Ty9HNd7t3Os/oizANra7CyErDfDzMlp/IcdF7F0Kus5h7iAJ7n4TruoUCiKjJbW3uIohAHjkhGv1I6XT9MlERyuQRDY4xt2aiaeihwAIcCx85wmx2pwTXlOplykpFpsb7ZZnEuS06R2dvbP2bFqwaL7BihT0hFv0HPuk/PD4PZ2HEYOw62G7C53YqlUnpHjjM/GUYcvtGOdbeSqzk2GaPOzFBIhkZcFy5fY21rn5ffWOe7ngkZd1/5xgP+wQfejWF6/OWXv8F//NxrJFSZF25cOPZ9xOq2R8ySou+q2+pQrZbQEtqE7trEcz3cSUN8GkeVmsWJJEqhkEdVRD716U/zvd/zYRRVY3d3h3RKZ2Nzk5XlZSRZxTCGyLLM9k4NQRRiR8R2p0syOXvi+1SrJfYnA4sRfbfV7FAo5NjZq5PQtFDoUVGolPKk1ABpvIPojZFlmdHIRJalYzpf+40Din0UODqdfjwY+XbFO8HjMSCJAlt941Bz+fTBvvCrbQ7HJE6ZKodw9yFJoUWp3XZQnQEzlRL14ZD5U2w2T8PcXJV2u8tq5ho7+/uM9eM16yhwSI6CNE5gpwd42XDxz/RzJJUEr9ivcxqpeHB/i/KT1wHo3LtJfqmKKCs037xF+cnruO0O1nBIcKQskEzqBL6P1ApVZCFUVU1oKr1+H1GQQBQxLZtyqcDYtNjvF9hxMoiyyDX1IQ+GeXa2PYqlAi++J8V6f4fqisNX74TL5QfeVWVsBAQjh4Hlk8mkuOiu8Hr/Jsv2EikxDPSSJB1SRdUSGgk9cYi5k0wmWLM2qHIVDWvyuDAQWObhBvrQy/LE1QqWOWR9s83yYpnN7WYcTBbnsqFboVriu4urfLH+JZa9RWZTFWwb0pk89VafhcUlFEVhbe3eoX7Is3Mvcnv/Jjc7N7lRuEHb6iAJiVjcceVCko2HI1byZ5MOokHEwPPpv9ycKAQPcZ8toc7M8MXX7iFLIvV2nysXlxBxqbf7PNxpxkH0hz8UlsT+4+de4wc/8BQv3LhAIX2wmTqtzxGq60rs7e2HlqxT2dzW1h7tdpd8Lhv3PpYWZuNZEIh8QWTW1h6wsjzPd3/Xizzc2GBpcQ7XcdirhX8Tz7PZ3t6NiSgriX0kLY3hqYieSTXp4NS+TkY6eZhwUdaxcwfOihF5YGlxjm63H2cUicZL2MVncNIrrPc0FuFQwNzY3I1nirTJ9R/1hQAK+Qyj8QExpdcbksudTeD4TsM7ZavHwDOX08Gf/87/eCjzOA1RsztapNbafS7k0ocYVEcxbozQK0kIAtY6g8fyBJ/GYGDQ7vRYXpzjTvcmANowBz5Y2XChPS0rAegLYV/hOWuZ/fsPAKjceILGzdtUL1+iV9/HGg6pXr7E2BhhNJsoly+Qc8PSxv79B2jpNNZwyOJTT9KXwLbsmO4b/RyNTTzvQFTP9/0pT/ER3W4fz3OJSIGiKHBF22DoJfDy17lTW+fdF57gzQdNat3wwpzNy/EOfLWYJdF4ib+SNV4sPHuiBzdBcKjEMN33mE3OUN9LMl9SWa9ZFDMyhulxZU7n3t6YoelxdSEsLa7XLDRFYLGcIJsMj/fm5ogby2FD9dOvdvm+Z/N85e4Aywn44JPZ+PYP3VB5ac3lg09mubl50Lifft+h24o/E4DpWTzo3ycrLmN6PhsPR3zkuePltdOk6Kdht01Gr4cZU2dR4bufuUIx9+iNy16jy8s3H8ZNc5iUYqZ21OsPt5mfrSBKoZBofb9NpVxAlMSYfNJotEPPjUQiZiKNLetQgNnY3EUUBARRmNCEBTzfn1B6Z9jY3I2VfhOahmlZXNM3uTNejsuT54Hgu5iNe9SsUJG3PGG+jUYmtuOwMD+D2nyZe6MwQKqKHPvLR+8R+aVPY2Nzl9lqiU63H383o7FFMplgY3OXYj5HJpt6W5Wt3gkej4F3X04Fn/vdXzrXY08amFpr9ykm1FPtWqPgEfgBjZF5Ys8jooY2Gk2SeqhfdNqMR6vZQcwJ1Ac7J95/ErRhDk9yaCe7SLbE3Npbl60GqE7KBT1FxZ2tYtshnVeSRJJ6gsFEGsWfsl2NJplDQoI/NRwWxLLjumDTtzdZwqauPsnL9w0+8twsf/G1Gqtln9nZKlvDEUVFJJlUeL1/k2Jw+VBAHo1MDGMUB4/pwAHwZO5J3uy9iVFfppIRSSdVhiObxsCnkhERJZl61+aFVZ21msvFSripGDsSt7cNXrwaMn029i0SqGgpD8cNyCdlFEVgYz/8bpfKEp4XsNvx8T2X7bbH86tJ3tgYU8mIdEeg6oeLUZdyaXzB524vpCOPWyGLK1dUTt10nCeQ+KbL6P4A33Bx2ybpFyuoxYPz9UIujec4bG7v4TgOXiCybx4PNM9fzpDPhXpc9f0mi/Mzsf9M1L+o11uYlkW5lEeWJARRoNXqomkq2Wz6kA/IxuYusiwhCgKiKOG4DrlMhm6vDwLIkhQaW+kJTKPLpUSNLeEa5tg813ButICrqkxmdJuWvEq72yOTTiJOMmLP85AkiWVlB7t4kJ20210SiYNSXavZQVVVPN+L14Cov5FKJslkkuzVGuRyYUZaqzdQlJAh9qEf/Ve8vj56WwSPd8pWjwER4dxKsdHE9fTuKXreWrtPWdcOsamAuCm5vVMjkdCwdDWUD59gWjjxpCZu5C8giiLlYp5SuRBKacunZxlHYSV7aMMcBTtFRzXYviGDl6AoLhx8jkKGm7fXSKVS4cCipuJ5fqzmClCrN7hx/Qobm7tUykVyN+/A1jZ7E+aW5/lx4Dj0/mMT4iE+P/6Zy6ZxnFAx1ik7eDWJJWx2hGvMeQ8QKZJovERGW+KavoU3HrBavHbotae//+j/lUqRgR0guRZKU4lLLmvWBm/23kRqSzy9HPosmF6fUbLGhVSVnKoDDrNZgbtru6iaytZu2OPY3dzm6QuLtFoNekOP+UqKTq9PbWdMpaSzteuxo97jxcKzBH7AF+uh1/yLhdANcrEos1VrksVmvlAkcDuMOZztRgKP1/JXudO7i16qs7de5MmLadba/dgE66TPD7A/NE+cNxETMuknpya262Paf76FupRGSkg8nCQZQrGICsymUzylhnMNtXqDlqVj+xKv3B8AYV/tvdcKIAhUyoVD9NR0Wj+2Q/eDgHQ6GfZDJvYEFy8sUq38/+y9eZCkd3nn+Xnv9807syqrsu6u7upL3ehCGISQEMgcw2A7AGPLmJ31eI3XXntmYtm1x+uJGcI7MbYjHF6HTYR3xxveNcbDMgsYyTBgDEJCHJKQ0EWrW31W112Zlff15nvvH2/mW5VV1ZckLxKjb0RHV1bmm/nWm+/7Pr/neb7P9zvCZnELVVWYGMuxul6kWm8M+6YHAUvLayi+xdPlEW4fPQs6sLVEx5W51MqSzmTIZtN7KLLJRIyeZdFst9EmbyG/9Th27CiipBAzVEyzFwYEz0P0TDxfQBKDvaZQhNfxSL/krGoxbKuLJIlkMykEUcT1PCYmxqiUa8RGs8iSFGUjV3JXfDXi9czjBnDrQiL4P//843uCx34n0GCWYCe2tqroeigR0bRNUmqYWQzKOK3NNslCIpQfMfSh8pjn+biOc92T5Dtrzysr63Ti+3P2d8P3FEQpDDQePl08jJqBnt27ehNbdVRZjXSSBgO3nY4Zalmt+0X6AAAgAElEQVQ126iKjOf7aKqK67qMrYXMn63pycgetN3p4vS9IBRFDv24zdCPW+yXKRRZwffD5qqoiah9T5ECl1GwuWAf5VzRpGCEQ2fxeJyDBRWlcYFmkOFZ2STfyzM/ud2LGazEF3IpNpodvFaLZCKOKIpUaw2ccQepIg1vsyM7WdDm2OhTbz3XYXoqlI8RBIF0Ohl9rwCLy2V0TSeX1XnL353k+uQeA5752ReB4Qn13YjLEnX3MuuXR7j9pEpWvTGHPMvzWGnsVVjeDfNiE890ccsWqbfkEfUrrz0LmkylUqXTMdkwwwzsbSdHr0kAcV2Xra0qExNjIRNLVfGDgCfOlJnJeCQS8ei8ymXTKIrC+kaRZqtNKh5nun/Ou7YbEVU6XkAuHp5nacnErq+iSx4XzDE6neHeR2E8T6vdRerfxB/51qPkcjnuuecuHnjgS3z4wx/iU5/6NO94xzv4yle+wm//9m/zyU9+kp96/z9lbHyceDzBX//1X/OhD32Qv/qrT/Ge97w76n3st+Db3NwKranFcBr9n338/3i9bPXjiJ1U3atlIPV6k0YzdG1L9qezA99nbaPE9FSBxUab+XQi9Ju2HcrlKoauodgyak7Dcz3Weza62e3fiFJRk3nwXpulMslEnG7XJBYzomGozWKZbDZNqVRG14ebwDcyfb4TLRyMmsHJQ0evWfqwi0X0mEZcj+O4Dp7no2kq8ZiB7/uYPQvT7CGKMN0Xelwdy+P7Pmos1CqShXDgayAd3u50SXdMAlnCbV/7JrcfRubnWHv+BWaOSHj524Hwe6rVm5FEzMDx0epZ2I5LMhkPL+5d6qeDADJgZg2CxOpajempLK1Wh3Y3YGJ8782+0ehx79f395vfD8994AeRNIrn+SxWG9vzNLuwdLnLsQWXkfgo+jXkOa4HDdvc13Ex3Bmf+reLAMSOpVELV5dvSTg21WotCiTHZlJM5cNtqtU68UQizK3EcHj2sVMluvYwdfmmCXFIqPL0mfMcO3KI3/v3v8/NN9/M6OgoKysrGIbBRz7yEf7yL/+SD9//sxhqjM997nOMjo5SLoeU4MHzH73zrXjVYYZceWY6mkt57vnTHDt2hD/5kz/lzjvv5B3veAeKonDq1CmOHj7IM8+d4uTJE5SKRXIjOZaXV8mP5PjuY49z8uRJJElkeqoQyfnUG609+l6JWAyz12NiYuw11fN4vWz1EpGPafRsF33XSmppeZ2ZqcLeBp20Q8PK81jc6SuhG4iyxEapjaaGF0xclpiYKrDR7LBpOWANX8TxZJJEPAwalXItuoEN0t/95LEPpA9xuXFxz++vhSQKrawZWc/C/vVzu1jk6JFDSJLI6TPnSaWSkQqv4zgIgkDM0CGnE5R9GgfnCPyA6cuhqu3WLVOohkawHjolWpYdyoGvrPFypRPXnn+BqZtP4CVjSO1lFHOdXO5mGk0RVZFxbBfP8yIl3UGGN1AM2N0P2YlBdjE9lY16UqPZ4Zv34PtJp2/spv544yx3Jmaw5QSKsMWREVjeUOgZu27WfedJkewrEjgA0qpBOrfddyu1zWiAFEmMKMDWWjvKSnZb9g7QVlTU8XHmAKNncm6lzosr4Tk0kZGhXqfR9aOAcWJGI66r2JaNJEs0mk1Ob8hoWlgeM9Ip1PFxLjXafPCDH+SrX/0qN998M+9+97sB+MxnPsPRo0dBVFEUhfPnz/PWO99Cp9PhvvveET2/KkvI/Sx4YmIM/IDRp58PD2k8xshI+Pd89KO/GJI7Nr/Fmc40sixzcXGZTCbF2bNh30nTVN7whjfwR3/0R/yL3/y1PT4e9UaogLwTsiRh2U4UFF8fEvwxxc7MYyC9vTP7uJLHwG5cqDaZTydo9GxkJTz+pV0rPKnZZGRyJCptDeB2HOR4WLLxPJ/FRjvah6sNObmOS63epG12sRK7JwGujUEJ6+bY8ejGOvh8AKfeQDF05sdGQpkSx6VcqeF6HulUEsdxMHsWtu30RRTD1fNAUkJoBUx0QhdCr//+nf6pmcrnKfZZX5N3vQmv3aNxaRHb7OHvoD9O3vUmKuUak9MzWL027maF+IE5rF6b9e8+iSjLKP1peEcWEEdSzCorQOgEVyxWiCdiJOJG2JcSwbIcxsdH2HTKtP0w65mWx6MbtNWzkJX+rIwkUq3WsSwbWZb3WAd7ns9Wuco/+fbd133cH/npH3C2fTZ6/JZMn8W0Kg2VsTZWTUZGVVRdispYu1FITNxwSetqGAomhM325uNbxG4dQb0GbThCs06lFX7RMclG2mfNrSgKsZhBD4ED6xssHpy/KnOxXm+SvLAYPQ6mp5ALo1i2Sw+HTs+n21c50CWRQiI29F4hrVgG28V7fjhbj8+ZnOlM7xE03dgo9f3IA5rN7aXO7Ow09Xoj6vVcTV17aXmdX/qtv+DZ1zOPH2+81MAxgCSJ5OLbK8RUbhezKpei2+2FHqs7oW6fV5IkDu3D1aZj5b7Ka6W6cd37OLS/iMSA82uXOXno6NDnX9iqEVg9yKSjYLKQS9Fqd1BVlWqthiSFvYxUMhFKqQQBnueTTMTwPJ+226WUmUDTVBQvCBlXCtRbPcx+4ABQJA0SRIFDmpzDWw+zAk1PkHa3B7KMqUm2Tp+mvRGWVyZuOkqp18OybfB9Jta36BAe93LnLHPxGnTB1m9mXJFomha267LR7NALRNDDUtXO6XRN10KyQNckmYyTy2VYWl7HdlzaHRPPDf3EpybHkSQR5QZNqErLJd44doQf7BB5BJif9lhcbnLwyBG++Q0JN3Ge9tosuUNrdFwPhRkcVoa2qZntVzR4jCUMxtg+by9Umyxxlncb93Hx1BaJkzlmEjE0VWatZTKaSFButxlNJFip9XtwqQwjKTgxOckL66Gcx3giTlKVht7XJDynxHyC+efOIx09DMkw+3I3ywir24zCpKqyeHB7Ah2AfTJla62NBZRr5StmTOx4n5lEjM7zLzBLBen2YRkXWZbpmj0MXWMkl8P3vcjT3fN8TNPENGEkd+XjPzc7ed3GZ68GvB48bgQ7krRUf/DH7zOkrjdwXC9bC/ZOw74SkCUF13tpxksSIq3s3uEqu1xGHR8WZ2zaJocXDnD+wmVUVSUIAi5trZE3ssQMHVVVEMVwalgUBWZnJjF7Fq7jUG23iRk6rusiiCL+gRnGbId2q03xmZCZpBr9wNuqoPT7ShvffzL87NKwvLueTEQ9D/34YRzHRRRF1gtjkSx7st6gUw5vhN2Vi2RmHPKCzQV/Fr/dxhsJp4Y9z8dJJJB2BAHLsofKEelUgp5l856v30LXffmeDav1Bm/OHsUNAh6vh0HkJ3K3MT+7wqULF4CjJL3DHDkK5R0ivwozxOUedTfM6CyvxZnqCxzPnXjZ+7QfTkxO8rhpMjo/zoPf/DK352/nYhFuu+02DuoJfu/3fo9PfOITADz+mc9w++1h7+m2226j3NzOhovtDkXCIDJ4X4Bms04qNYbytjGcpSWkbBZRVtAOJODAAQB836XdbnMilYmC0QDtU1XcsoVveRgLKURdRCvEECSR5hMlxLiMZMhDXvM7sdLuwsF5YrLE+KkXYQdbLdf/N0AwkWdLkUnEDSRRRJ+e4PLyGgEBK6sbtFrhIqswnieTTl3b5vZViNfLVjeAQdnK93w6XZNys8183xBqIGdxNZOmC9Vm2MvYZ3J84NcB2+ytaqc3lJ0AQwyeG8XgM15q43yAFg5v6hvfnD5zPupz7NcHCaohw6zVanNyIby8fNVgaalOy2kh2iKH5mcjfSNVkaNAvLKyjuv5yLKE74cyJvt5cBw1ljnVmWW5CvM5f8hnYeecSFJo0QqSeJ6LKEroB1S6l/rT46qCJEm4rksum0Y+9eL23zANCcmkqRiomVtwbJeuaUYkBcd2WWp3mUnH0KTwO7R6Fm/58rA50EvBMz97hrW1IoVCnrsfPIrpX3u9p0sen377U0O/25mF7Cxfnam9QFYap5B6+TIZX/nq11ldXaVarTI9Pc0v/ML9ADzwwIPMzs6ytbVFPp/noYceYnp6mp/58M8Sk2UeeOBBbrn3XmCYTqzpCaxem7/5T/+Z1dVV/u2//Td84xvfZHZ2lmPHjnHq1CkmJsZ44IEvsbq6yic+8Qm+853v8Na3vgXPsfEDnwvVJs0nSiij+hWDwk7YdZvusxV8yyNxS+6qRICd+1op1+j2QivearVO1rbxV4ez/NZIjraqMFkYGwoWW1vb9sK//u8+/ZqZ83g9eNwABsFj5wTplTIJ3/NZ3ywhiSFDZCctdD9cr6HTywkeO99jsX3hJWcgHj5BTUW0RSYnxiPl0vxIlpVWdw8byC4WOXp0Eqlf0K7ZFuWlBgfnZ1ld26QwNsLy6gYjuczQsBWEE8q6puL5PlJShD7ZKhYz6HZNBEHggHSR71emKHclDo0yFGR838cw9NBJkGYYPBwXNadi193otbIkIclh8JBEMfI793yfiX7ZC6CVgUI6zL6WOlnGDxzd891tbJTIZdOvWPC464EjN5zBfO6dT+z7+7gs0fHXcHyH47kTVO0aOfXK+l2vBL7//5wm8/bCVam9OzGfSSOJQSQHsxPf+c53eNMdt+77nGN1kRQVUZRZ2iqy+UyYcanTievvweyAXbdpP1EifjKD3/P3DT45TaVbr2NoGu2uydzsJPVak0x2n3vCpSWCvgcPgJDLIB6YhR2zHa+zrX6cEfjEE/FoYO9QJslqo830Dl2aej0MFANe94Vqk5mEwUp7fz2dK2FnNrIbLyeIVKt1ctIYJa5/8nwnBuWrrDUVGSutrm2yfHmFsYlxtmx3KICoo6OcPbuOMaqgqzFq6w3U8bAHMDc72ef1j7O8vBqxuTzPp9VqI8sSgiiiShKCJSAqAj3LDpvuihLy8T0odyUCRFRFxPcDFC1ka4miSDxm4DgOmq8QyOFKst3qRoHD931cICDsw3ieD/gRA6aiaTieh2XZTCWTdPo9mFF6+NYzpHMWtMBRsgTJGURRolQepn++HLwSpa8BOq4HFFAYMOYkqjQ5lEnudbt8hfATv3ATT381VHtOvXl/T/idWKwPSljNPf2PN90RBuSBvtjupj2E8yiiLqJkVbSpl64XpWZUcu+Zjt6z+UQJ3/SGZlyqlg1GDMxr+8GIB+fg4Nz2L9odvDPnYIeQYuBdn4jqqwGvB48bQOAHIIhUKtWIaSGIAvk+O0cWBA5kk0M03Y1muFS2cK/a66jWGgi7bC09z7ui0dONBI6vfPYZAN53/21DA42l6ksLHgAxJGraGisrDjMzoWPa6TPn0TWVhb4U/Y6dRR0fxwM6HqjjYWax0ewwkYqTz+dYXdvk6JFDnD5zniML88iKjCAIzExPUCxWQj/xvgeK7TgIoki3a+L7PpNxCHaYYkpjEvGe0Z8nEfsDjCKx2EBgTyERj+G5Hs3+3Ijv+0My8YoiU63WQ9tfTUX2fXRNpSOAPTdDbmkF6Y5bqD5/hmBp8Nk9FuOrjComc+kbWyi80ljIhQF9D6X3ChiYgA223Y2qXcO0OzSdBjExRcYYJS3rQ6vmqyH15jHMi02stfYN3dAH/Y/rgb3ZpftiA20m/rKCxn4wDqXCzMPzMS+3ccq9oUDYM2JM6mF2I4jC9Zm0JeJIJ4ZVEATpi6/ofv9j4vXgcQMYrMx2q4ZqsrSv9IVlO3Rcj7gshZTbwASxXyYMti/q3WJy+2GrHLJTut0OqqKGBjiiiGU59CwTTQsbzBOFsYgquztrGUydD547ljvxkvsfA/bVZryEc8llbm6a8fwoFxeXmT6012gngmVh1+sImo42ng/1vjQ1CmhHFuY5d2GR/OjIHqrr1cJlIS2x2fAQRJFKrUHcCJvfOyVOLNshkAN0TcXsWUiyRDaTwnM9HM/D0LXI4dC7wgrwxbVLHLx5mrZ6ELHVoZVJk+ufD61Wh9lKDals0qnfmBfLlXDb54+/pO28p55j+o5brjlYuB929650SWQ6neVMJ2xAd/0m3U6TQTs6paSRfB030Pf1JBnAOJTC77m0T1VRR/VrDhZeE55P+0wdv+OijIYLksHsyT8aJBHjUAprpYPfc4dKces9m4WYTjIRJ51OvmyXz1c7rjt4CIIgAU8Ba0EQvF8QhL8C3s62fcAvBUHw7D7becAP+w+XgyD46f7vr2v7VxcElpZX93UhG2Ahlxq6+AaT5AjdKypSSJKIZe3VGVpbL+K6YRPe9bx+HX94ynY/DCald2MwKX2lUtiNQjOTCKpNJVOldy50qStulUmpBiWu0E/RtKg0tbS8jt3pUB0fp2rZLORSrG2UuOn4YU6fOU8ul0GSRHqWtSfTajSaSJIY/T6mhAfXcRzSQpy214ka54OmORBOuPe2xR53ZhvmLh8Gs2f1t/MZhK47jr6BKjWqjQaGpkezHQNfCCeVoKvIYenr8ks6rK8IpDtuwXvqOTq5LAsHZ6lW6zQ9H1e68e++54WNZ4WZfZ83o1P32gNuoi6TOJnDWmtT/doqxkK40LqeZra11sY830KbiWNeaIYN7evsZ1yL4TiY27peZO6doH2quofiO1g8urb7Yx044MYyj38FnAF2fgu/FQTB56+xnRkEwZU6h9ez/asKc7PTV21uD06eG6HkTk8VWFndYHMzFH5Lp1OsbxRJJuLRkJrrhLTVUql8xfcZ9EF2Bg5vx0pQ2+Ersp8e143CNTrgKUwoo2yMl6MJdM/zmU8n2Nqq0JbkK656p6cKuH2FViGXC4cnpwq0Wp0ogNx0/DDTUwU2N7ci0TgHUBUlMt3Z1N7FsZmAwBNwHCiX4dD8Mp7vR3//7uEzdUcA9fukEXVWho1Q6ttzPWRZRpRERMKs0/N8qtU6+fwIbXmVRCJO4PtRudF1HGRFGQpOPyosLa8zpWvEqzW8ao3cHbdEVNJBP6nd6SKoKuZLCCgQUmgdx+HjH/84n/zkJ/nOd75DJpPh2E0nCLyBVtn2Odc0TVKGQdM0sRNxRt90hBfW1zkxOcnqi5dZfKwUNdb9nkv7uSpu3SZ+MkPnVJ3Em8eizGJnsAkzowTnX7zA4WML0e+vKq+yC4IoRNfr9QYSdTqBvdndk0ENrv1Wq4Ohacg3YOj2WsJ1/VWCIEwD/xT4D8DH/1H36FUM3/OolGu0u2E9u9B3RRugbTsUEsa+gcO1Q0XOQLBxbBFV2b7B1BotXLefWQgCpVKZyYnxvl7SJkvL6xG76Ep2tp7nE/g+vaZD4Hq4nk+ykIhW5sEuiusgcLyc0hWAKDn0mi63F47xNC9y+sx54vGQipxOJcjqGivd4ZvphWqTmVSc6lYFz/c5vHCAtfUipm6w2GgTlyWSEAWQVCpJzNCj7MF2XIyYEQWPWCzgwQcF3nlfgK4J5EbAd0NBRUkSw+xiVxYyGOBKxAz8IMB1XRqdNqNKFhwXpABFkZAkCVEUQzVVUUSSJGy7xmgySyJusLVVJR6PEYvp1OsOltkjCILIue5HBV3TEHpWlIF4Tz2HctthfCmGJIkR0WGAgQ1sW7n+/f6Hf/gHyuUyd955J47V5fjxIzz66HdxHIenn36ao0ePcvZsOB0/MzPDY489xn333cfZs2cxDIP3f+ADPP7VrzLz4Q/RSIXNafNiE6fcQ5uOkbpjFPrn7+4exn4Ls1gyOfR4IK+y2mjf0GJuEEiupeOmZlSaT9TxOu6ezGnn57U7Jon4K1PGfDXhuqi6giB8HvgDIAn8zzvKVncCFvAQ8DtBEOxZcgmC4ALPAi7wh0EQPND//XVt/2rCzfOx4K//+NdCjaJUkl7PCvsMO8pIq402Pc8fOlFbrQ7lSo35A9NR32FtvUg6nbrqSbWTpjo9VbhuhpXbr9kPVsDf+/tQz+p99w8L8m1tVcnnc3iez/m+de1LwcBYyjCzlIzS0AzIflpYhUScmBbnUqVE2nPxfJ9sf2bCEn0SWpZOs0I6k2NtdYVavcFILkfPsjhy5Ajnzp2LjsOhQwsUSwLjY+F5fPmygCwtkU4LVCpOZDIliiIZzaHlakOmVLqmRRmNbgi0W06kL2RoWtTnGgQuSZKifshA9LJea5BIxNB0Lfp+Fy+v8sGn3vWSj+nLxRMLn6c1PRk54UHYB9kN6Y5tXwq5vYKbmKHb7bFVriIb+lWzklERyn6YgZw6dYqD/WnsS5cWOXr0KNXKFuOFSVZXwxmTra1wUbRVrnLy5EnKzQajqTTNZj0cwANiskTX9UipCvmYfkMMsLW1IlNT+5eKrkWVvxIG1/PV0HyidEUW2Y1+3o8VVVcQhPcDpSAIfiAIwr07nvpfgE1CAY2/AP418L/u8xazQRCsC4JwEPimIAg/DILg4vVuLwjCrwK/2n/4F0EQ/MX1/nH/GMiP5nAcB13X8DyPVDLO2nqR8bERZFne90RLJuORk96gxu66Hkp/sG5wgi0tr2PoGpIoIogCuqaSHw2LDat9e07HcZmdmUSSexDEqJRrmJZFfiQbZUFr/TmDnmVftQmfz+eipt41J8+DgEy9yNjmIqptYqsGpcI89cx45BViGjViSDxZfZY35W7lpuOHqZRrjIxmh1ZyX/vyf+HDH/4wMwkDx3H52te+zh133MFTTz3F/ff/HA8++CVqtRr3338///n//Rzdbpe33nUXrWaTI0eOhPvj+wiSxMWLF1hQL/P9Z6fY6iosjPrYgG4YUXYRvjw87p7nI4oCQd873XbskMkFWPZwmc+0rOi5eCwM8j3Lpts10VQlCh7tronU1+qSZYl6X6nXEN3rGur7x4DkulHgOH/hMgdmp1F2BAoAt1HBe+o5goWDyJntVbsf+HsyXNd2qTWaWJaFq+m4kkS5f6heWF9HyOUiiq2Qy3FuK5yxKK+vgxgeG3V8nLIfPj+Y/i722W6D0tPLwZUCB8ChTJKL9VZ0ve32SL8SptOJawaQ1B2j1B/Z2LdZ/1KD1msB18w8BEH4A+C/IcwcdMKex98GQfDRHa+5l35Gco33+ivgy7v7HNe7/Y8aN8/Hgi/+x49jmhaiKDAxMRY1tZOJGK12l8DQEczevtaX9XqTWsfEMwzicjiHMBrTUSSRzVIlUqCVJBFJFPvllHCVWxgbYbNUiVbR01M5JElnZWUdXQ+9CgDmD0zvyVB2UnV3o9sdlH50LqxdJpDtPcZRst3jxAvfRrNMJH/7huyJMpZm8MLJe3CV4R6QX1Q5eiR0Drp0eZHDhw5FE/bTo3meeOwxCoVxTLPHyZNvoNmskkgkWV5aZmp6hk9/+tMsLS3Rbrf54z/+33jxxTMcO3aMSmWLSqWGJIAky/hBwCFlkacb02w2ReZz4UUuinszNENy6DjS0POSAN6uS2AQaERRjH7O9r9LQ9eQVZd2y48yO8dxUPqudwO3Q9dxkFUl+v52vrcoiiQTMSRJotXu8FOPvWPPvr4cGJLLo4e/xFoqdV0WrPtlJKgq0s3HaXe6dNommXRi3z5fu9PFNC0sRUEJAkTXjZwCG+KVGV4pVSEtb5/37XaHXC47NCA6eJ8bxeLlVSBcHCXie/sRA9zIDf1aAcRaa19xkHA3UpqKpgjEJWWPuONrKfO4oQnznTd5QRAmgiDYEMKl2Z8AvSAIfmfX67NANwgCSxCEUeAx4GeCIDh9Pdu/2nDrQiJ4+DP/mqrSQ+krFia9bamRgQTzhWoTw3MJXBcpHqfXaoPn4SUSLORSFIsVYjEdVZGRFSV0++sHDNfz0NSQiptIxNkqV1EVOfL79n2fbCZFJiuzshzaXz750BK9/o1MFCV830eRZSQ5PDE3V8IVYWEmfcW/zbJsbrl3Aju5q1EYBNzy7DfQzTYie88VHwEzluT5W+6L5jBg2wNEtEVuOn6Y8xcuR1pXwlXE4RZyqUhkclACsnoWFxeX0Sc11I6G5zqAyETBoLRlhZlHf8L8xFRYhvJ8fw+DzZAcTE8CQpaWLEm4Oym5vr8dSHb0SSDs30BYtvJ9nyAI6HTNyBY1kw4DfyIRx/M8qrUGIyPZPitMgiDA8/3+wKKLKAj4/UDz3m+/7YrH40YQExwenftC9Lh+YHaobNVqdWj0FV/3I0tIvQqeHioneBcuQH3bO0W64xbanS6u4163HzhsmzKVq3UkURzyRhnI3+/GYEF2I97jV/rsKzWrX0pGcK0AUv3a6tAkvSwIuDeo4PHf/dof8/ylzmsieLycnPo/CYKQJySgPgv8GoAgCHcAvxYEwa8Ax4H/KAjCgOv4h0EQnL7a9q9uCDRsEeoCnhj6GZMkMg/aWSKKKzJt20a2bWKyjGJoiGJYmor15TIaZqiHFYsZe6i19XqTUqmM0RcAHGQTmqbS6ZjU6g75/AiB72Pbbv8GBV7/ZL3RFZumqaiyym7CcKZeRLPMfQMHgEiA3uuSqRepZ7dvSBnndqqBy02HYpwv2pw8+QYePdXgptljAJxe7pJLyiQNibgucXq5y9yYxrPLNnEtx/Kyx6HCQZ640CWfL7Jw5FYsr8Opkk8+LTOqx7FoIScVHi6OMT+psXVhJaL1Sn09q0EZKuxr2Pi+gSiGGYLreQgIuILPX3VP88/048hCX9pEFNFUJfLzGGCgCOz7QcTY6lkW1ZqHKEq0252IvWV2zf7ciBPJpQxllvvodL0U/N1bvkkqmaBaa7CpFEJP8FMvMjKapXlpmXi1r2ArisiOy+jcLO5mBXZ9p2FO2WfzJTLhv8Fzm2V0oHRxkcxdb7rufZNVGRmZqclxVtc2sTwvslYeuAUqihIqAPQzm6nJ8SHLZbi63cDVPvtKWMilcPsU5GsFEFEQubi4TD6fY6tWJ5GIk8vlaDUbjBcmaTbrGEYM80MxkskUrVYTR1Jwmw1sTWUsfv3+KpLwmogbwA0GjyAIHgEe6f/8ziu85ingV/o/fw94wxVet+/2r3bosoyWy4RWsf0p5MhGdscJ32i2SSZi2I6L4zhk0jk6XZOpiTHWNkpgEt3cZKq+HLgAACAASURBVFmOVtme59NohKsiVVFQVZVer0ciZtCzbAgCpqbGWVpej25OP/WLb6TUMem4HnlJwJMVql2TnCTQanfpfTMMCe+7/7ZIPrzTNff0Q6yehdyQaVGNSlfjm4tDpar9IPkuY5uLQ8Hj3JrJaErh9HKXe06mefRUmP2MphRane3VW8v0aJkeuaTMUskil5Q5ORfH9T1eXOlxz8k0SyUNSTA5teRz7xtSvLDcpdzrsFX0CfkWMJnVeYEwCIqCgCxLERsLwPcDNDmUxhrcvAVRxDR7/L29zEPOKhPEeY86O9iin5WEmYmmhZTTdCoZlbJihoHYn/OQpD6DKxBIJOJYlh1pZnm+jyCEN+qB/Immhvv5Slj/yJKEoijMz0dkXAb5VMfQcQ7M0rNtkq0OfrlCaYfE/UvB+nefZHJHAIn8L/ZBtVqn0Wwzf2Ca6akCa1vVoSZ8SlWwrbDk2utapFQF3XNptbtD5+jY2MgV6eXWDmr09do0w/XfqBUtxqFDh/j93w8dC6empvjLv/y/+N3f/V2++MUvMjU1RbPZpNlsIssyd518M3/zpU9x/899mKZl07TsSH1iAFEQ8YN9vv3XTux4fcL8RmA7Lo8+ffm6X398KrxJjWaTNFsd8vkcVs+KKL6Ll1f33OQ2Nkvb8iGlCsVSmfkD07iOy4gis7FRYmV1Iww8vk9bUfE6ZqTUu7K6AUFAStKoSgoLs5O8oG8LPEiSOLSi2wlN1yjoGu1GFa2Rxko0UOzrk9lQneHXHTpwIfq50TnE8dmAs+2z/OCFsJSVBJxd8k/ToyOYkswL66F4nKAQ/VwQU6TdizzzDLTH20yaE8zEQBJFVEXh3MVNIKTUqrqGoighVbWfgW1nICKCICDLchjwewEPOqFx0Bfti/ykPkNM0bAsG0VW6FkWhq7hjIbBVKpt1/GLW+Wh956eKtBud/BcF0kI+0jNVptBaXjwuqh5P2B1SS6m99IvRd8PqFRrxOLDZUmrZzE2NhoypyQZ+pmSKMv4rsv8fWGvpXP5MqWL4TEYOzRP6eIiiYlx2htFxg6FDKrSxcXoubFD80N9EoEwxEq7GvIQWhXsPL8l30e3u8QyGaqWPaRLVdBkREGgYzq4nkfAXnr5IIAMFltAxHIbGcmwX+hYWl7vW9wKmD2LwwsHgL2ePFeC1Wuj6Ql+93d+C0WL4bsOTzzxBOe2tnjP+95PTFPoWg6qImM7LpeW1vjgu3+OwuQk5bVQAsgNgqhUdiQ/ito3JfvCF77ABz/wAb7RVxrudq+tkfVqwevB4wZwJeey/ZBLJ7hcdrjz5CiK38FIGBBsr4w2NkrIkth30hOHbnL1epNkMhHNFtTrzbCPoSiIogT9vkgulyEPkRe6pipRKURSQ6OmaqdHr29hu7S2ztxUeMElk3G63R6xmB79b/UsepZNvJvBFT0swFYN6NT3/H27YStXphxfblxkQprlmHKY4ugaNa9DvLg3gBVyGVzH2VeZdzfn3k7YeHWBL8qr/JI7zszkLImMgE6HZCqD51YQ20JUxtsJsa+TJYoi37RX8frrfzfwecha4d3+LJqmomkKohTa1FIJS13s2K10KlxJlls1urLFi5Vt97pYoNHt9ojHDNqd7hCLC3YEDjo8/bZPA6GbIYTy3j/5yFuveDz3/D2igOsEVLYsMjmVwU7KijJkTexuQnvXtosPPczc298GF7f3fWeQ6Ha7xMfGmL/vHSw+9HD0mt2BwnvqOfADEAWsnsVWpRYOdxbLQ9mCGtdo22qYRRMaLPVw2Go7bFphhqv3fTJ2N7thO4DsZoPFDJ24YdDt9kKxzB0o7FPu2l2uqtebV+2vDIQYB///6sf+OReqTRYrW3teKyRUlh9Zp5HwEPa5ZwyytE6nzR1vfCN/8Id/yC9+5CPMzs6iqdefOf2o8dpzIPkRwvV85P5N7Z43HmEyn+GeN54EwtLBnbfOIksS73zzCaqNNicWJpHk7ZvGoMQFYRYjyzKThTzTU4VoNQV+JJOh9i/+RrMdUj91jfHxEWamJ0JXwEqNUqkc9UcITBIxI3Tna3u4jktKldE1BVWRmZuaZGV1g61yDddxI2ZLOODWpNnqUG+0UBWFZCLO4fRxSoV5PPHqawxPlCkV5q/6mg1vmZXVdRbSR5hxpumMd+iMd4Zec/bcRS4uLrOQD/s/WX+YqaQaYcBJFBOUvQrfktb5cv0cn60EGEaWbs9idW2KWCyGbszi9Rvbu2/cvh8Q+D5tx+aL9iWsfvCw8PlC7yI9zwmHBpttFFnCtp2o1Nfry5t0uyadTlhaGSDnbZcl5J1Mo/18qYOAA+IFLE9m0T0IhMNk7Y7ZF2u83n5IgO2E0vKCKCBJApIYfq/tdodWKzzGa2tFShcvRVnHyPFjLD70MImJccwdLnzoWhg4ThyjdHGRWCxG8bkf0rl8GYCxyfEoS9mDTngsNF2LAsbu0mir2iImbx+blXZ37xR4IoGUTmHoGpqeYLOpoOkJND2B7cWZnTsUPRYFEU1PkEqneOyCjxZPk8qMYMRTyKrOcsPASOT4wbIc/j6RQ9ND4srfP2tje3E0PcF4YTJ6r8HnXAtXy1oy907QOb3/omuz0eLU2hqbnS7x0VE++qsfQ0lnqHS7iDfoNPmjxOuZx0tEKm5w7OAUAwtt1/MYSWV5713bje/1YpVsLD60TavVRlYUYoaOosiRI93m5haSJBIzYsQTcRqNJj6gqSqWbWNZDplM+HV5XsgkGhsbYbNYxnFcBEFgdb2B5/nEDJ10IclmqUw6lYzoomtrRSbGQ7aL0HO4dC40q5k6NBYFrLnZSRqNFt2uied6FA6+E2/5LEKnekW2VU+PUc9cW8fHiBvR4GCBUdbWi6yPh/uwMxPZOVxY3cnOSiXADG+G6qbB34qhYdOX/Ce56eGTpIxx7r57sI/2nqBhBWrIlJJEbMfhq9ZSlHUM4OHziLvOe6W5sKzV7uCL8CnzRX5BPohtOyi7tMFUFMSeiCf5xIJw5eiKHgqh3PtuKvDx+CpFZ4TF4CA+AQzKWv2TaXQkyyPvehLbdkjmYvRaNj+4FJYzJlIusxMjLG9UaPdgNH4cVT2KbZ8NGXklH0VxSRKqMgdBQKlUIZtNY8eP0n0hnPiunAmPXXujOJSNlF54cfj/fqAY/C9UaoxrKs1LyxR9G8cJ933U0GmsrSPrOqIYxssg8Ie+A0NVSacSOJaF1OmGxzGRiBhMutklmU9jN3vkR7NUq3VMJ2BuTKNSCd0hU6ks1ZrM5Vqb2+c1zm5C0rBomSrgcGnTYTwDp5a6zOUlOlZAveOSTys8ca5NXBOJqT7JmEYq5rFUNrnJUHHsHueLIuMZh5GkQDIuYw1Lne2Lq02i69OxIT0ubSqGqMtUu+E5bDoOZmMg7RdmW/4NsrN+lHg9eNwgXM/jnW8+QbHSZHwkxaDImkoYdK2w0ZdLhSu/m4/NcvnSRWamMmyVTNS+W1273YkGDMvVOo7jkEzEQ059p0s8ZqDrOtVaPTJZarfD0ofrOGyWKoiiwFZ52/AIYGaqwNpGiXw+R6vVQVPVaApe1zVcz2OzVCYIAmamJzh4YobWZpvSVuhiNjMzSbtjIslh7dZ2XNpdE+3g+zhw7suoTmvPnEdPj3H6xN1DNN0rwUo0uGn2RETbnZocJ91Jsby8GmUhRs1gMjvO6TPnOTA3w9RkgbX1TYR0OpJ2t4tFHg2K2H39JESH9em/R6gf5dy5cAjwXEkARBZGQ590AMlp0HFAHpNprHb4gnkhyjqifcTni/Yl3qlOM2LEcByPL3Uv8Y3eMlNGjHeKUzjOlQgEPuKOZN5y7SG68EEjHPRcl44RCAFyEITMrf4No9XukIjHaLc7yLJM2+kifv8fGLnzfpJ6j1bPZ6Mp07WXaPRCJtRbboHHH4dWVB6xed9bZ2klJkkA3a6ALK/gOA49y8Y/MEN+JMtmqcJA8HGg6OxtPseqnUeSwr5QPGYgSRLJZJyNjVJoyNXPKGKLyxzcQeUlHmPsyKGobLW7cd1shBRhWVHodE1M08R1FWZHwsXWVqWGLEnYDZOtcoWtrTKF8Ty5jIvVs8imszhu2BjPpUzwmiwutjk6N40kiTiuw9HJGPgOptXh5imbbDrLzCjgO2QKYFouhqbSbHdJxTTedlQBUUEUZSzb5WghDECO7SNK118+0s0uc/OH2KhVaO74vgs5nXIWVMMGVae31sUp9/BNj/TNGr2ajLXSIXFLFtGQr9ss69WC19be/oghCiLvfdvNyKJATNtOWd9/z3b9d/D7hUKOtZVldE2jUSqDGKfe2J6hSCZimD0Ly7IZGclSqdSQZYl0Jo0kSWxVysiyTGF8lI1mB0OWcB2HRrNNLpumWmsgSWJfXNBlY7NEu9PF6DcPp6cKUWNc6/tr57JpHMfBG/MorlXCZrCvImvyEN++UqkRixlYVnixeoLB2SMfINleI1N/HtUxsZXtCfPrCRwDvFh9gcLoDOcvXuTwoUME/cB3Qj+GbdtsZle4yGVuP3IzZ89dZGqygOM43JTPbq/wRrL8l60nsHeUm/6mu8rHWaDYDGjZMqrscnhMw/RdPD9AEgVatgL4mGs9vums7ck6BvDwecRZ5UMcxwo8Pm+GvuGf613g/RNH6LXMoUHC3RBFIZpM9zyfjNxhXKlw1gzLmqJoI8tyFDgC348KyI1mE0MPdbvabidSIW31fO6+eYFkAs6dO0chHaoNKErA3OwSPctH01T0vqaW5bfpdD3mCmm+8r0e+ZhDUhdQVYWeZaOpCla/WT2QhtENHckVol6FKAg0mi2qtXB1vFOBuDc2SmJ+NnrsPfUc3tPPA+FNZbeg/SCv9GWZqUPzkeJsvd5kve/UmE6nKBTyQ1L8vtcfxOwHDt+zaDZaiIJANp3k1OmzjI7kQhqv7/Dwt+LceZfB6edEWtJ5jufq1FopSv3KmN8/0MdmIJXWSKo+DvDww1ne+ZM+j31X5O33tKLPvRZEQeT5539IPj+Katt864EH+KVf+m8xDIM/+7NP8i//5b/gf3/gAX7913+NVqGJLIesPUVROLdVRj+QwGvaUYkrcF8J/t3/P3g9eNwAZFmkUtogk06AIHKpWmMqlSJdexKALf0NJJNxHno6XGEenUmSzxlIosHSSi2aQlcVGbNnMZrLUKs3o3JFyNWvMTcbTolPFMKLyPA9MpkUW+Uajuti1hromsboaJZSqYJh6MzMhA3EUinMIlzHod0OnfT8fllgMLgmNgVqboO52UkuvbDCwQOh1Har1Yk+v9XqYFlWVLteXdvEGT3EpekMQQ9UNxya0yyHuJyg6bSw1XDVJUpXVzLVkypqXcPqWWwWt4jHDaq1Os1mi9GRAn6lynnO0Bl3WFvfjLYblAge6S3j7SqhBWLAee0CN1lHKaQlpkZixGhQbMsocihwmE+rrFd72IHHg87inqxjAAufL9iX+Fn1DTzcvRQ13X3ggfKL/HTiELlsur963zbmEgQBURAQRBGhz7lcUM9z3pqjboa3z2w2TaPRJJfLsLyyTjqVIJ1RoALj+bA/UG80SSZiNP0ijG0z2p99RqBahfe+Jxyc3CxVaNYrBARRU9jxfLqdJulqjfTUJO1mnVtnNS6WRLaqPmEvJcwYRCCmOEhilUw2jWoFOP0Sp6rIfV8TH0PXEEQxWkwApBIJpL6I4mBAdT+21QADHbXV5XWSl5dJ998rCRyZnqIq9inO/aylWAwXN1cSAh1gejI8P0+fOY8XwOjo7awWt7j19jzffc6nbkpkk02mYzESfo2ntjL4gsFysQ3FNl27n7XPHOb8WZGZGaLrN6aKjOcS5Ee1ITfDnVC0GO9/33v4vz/1Nxw9epR77rmHJ598iiO33MpHP/oRHnvscX7+5z/MY489zqlTpzh58iSpVIqR6fCaEyQROauTzIbVCkF+7bShX/cwvwHcupAMHv7sJ6IJ6N26VEeNZbaUefT4CJIo8vBzYZ1WCFze+cZpFi+vMlnIoxkeS0t1DF0jkYhTrdUj3+yBaKIkhgyqudlJut0e1Vp9iOqpKHI0rDboU4iiSLPZplDIU6018D2f8fGQHz9RGKPbNVEVmU7XJJcLyx6tzTaxfFgqqTdabJk17jh6Aghpv5MT43S7Joau0emapNPJPcdlaXkdM1GLHg+EEq+ETDyFsyXTarWjAbFcLixfJBJxdFVhbSOkLD/deJ54MY7RN3canxrjg+c/TzfY+/56IPM/2fcgo3BwVMbQwsl5Q9cQBIGU5rNeDec6PudcwL1KU1pG4Bfix/hC9zzdYLtMFUPmz9P3IvlCJDUyyDRkWUZAoGdZHDQ2sTyJTW88mlz3fR9ZlsLyTFT68pmby6GXno/YVo7t0mq3WfMXyXszfK79NW5q3McbFw6Ty4Jjh1nnTroqbEutf+vvziJbFre/P9QBG0jT71SBtnoWtVaXpc0m/+rcT2IF115HaoLLf5j/dngcVJH5/CHm5gKeflpgs3cegGMzKSR/HD3mE4uBKg2TIgakkMJYnnqjzfj4CN7aOmzsZS2VhTA4jQZ7vW4AYm+7C8cNUGSBVicgGReix888E0ryHDk0Hw0LSr0KSyWL6emJyAHx/HKZfHaOcy+KxONw+KjPhXPP4ngivUCm6yhRtjLAm4+mePTR7wJwyx13kTA08iPJkAlJyKKqWja5WIJKu0HXdnj20Ud570//NN/6+td5+7veheOEf5MgyXzlb/+W2++7D/ivZ8L8v0pYPYtcLkPRXut76YWN6LnZSc6vCqSdOklnkbPmLG87mec7pyoEgsz3n0wyM3Ocah3GlAbgEwRg2zaJeCyiCdbrTURBIJ8fYX19k9W1TXLZDJqqYjsOhfE8lUoN23HIZjMYusbq2iaZTBrbsrAdZ8jTw+pZBEEwNN8hSdLQQGO3a0YltUJiJJqYH8jCV2sNfN8nl03vO6gV3sAm2SyWqStFPMW5agAxmx7z0zOsrG7QaoXt2larjeM4VKs1UqlkX/tJ5Pb0zTy2sk4OE8Mw+PTlH+DuN1wF+PhcTq7y7thBLhVdbp5RAT+a0t/qC/c5QsCUmNi32jZYSwmCwA+t8h6qr0dI532fMY8oyIiihCiG0u9ds8eYUidl1FnhCG7g4vX7MoauRRPSe9WRu/1jEN5obcvGiBkkSnn0jEqyG16mP7hwntsO6CiKQr0/SFqt1oknEmiqjO/5dDpmNHw2CFAjI1kCP9Ti2ixVyGXDeRBVEiiklesKHABWIHPf7eF3r+kJIKBrOUzNyGyGsYMXV5qc2CFQ+NDTm8RUkTcfH0WURGRZwrKOouktOhujPHbqIvfecYSnG0e5/fbhQCTiE1McTjlZjs0XODiR4NvfFrj1dp/1Z5+he/oyM9NZzjyX4e674eLFCzT78iuGYeC6Lstr61FmNKZ30YHyUhioXGOShelRoM1PvGn7ZBiQNa6EJ596lvf9k3fxp3/255w/f56RkRF++Zf/OZ1OGySVZ599jq9//ev8u098gmk1R6vV5O83N3nyu99lcXGRymc/S6USZq0f+43f4P77f55zW1f26Xm14vXgcQMQfId46wVcLZQcP9hfLU9NhXIK8ZhBzJHZVCZYUDdYb+gsjPpklA7GmM/qssjISOhQNzc7TbVaj4LG0vI6hbERGvUmiCLLK+tMFvKUylUajXBy1XU9SqUymqZiWRablsN8X4W32+liGDqyLLG1VcV1XQxDx3HcIRmMQeMzEY+xtl4kJcZJJuNUa2EZa3VtE03XqNYa1BtNJifGmZudpFqt02p3SCbie1a8AxTGRykwyuL6EpbeRjbjoWHUbnRkFjur5PMjQ9nHzHRoLuQ4Hroezp80Gk0sT2ZyYpxza6t8lvN7SlYD2ILPg+5lfnn6bcxOy+A5jCsKzWYd13FIJn3qTfgZbZ6f0eZpui1yWmbPMFoQBPgi/Pf1b2Ltqt5b+HzBushP6rPogowoCiiKTNIvMqlWuOwdosoIrheuLFVF3qPf1G539s3gej0rpBf7Pk7LQ+lrn91kvoM2LgEijuOhKAqZdIpqrYHtuFiVKolEuBBIpRJRvyWXTUcLhMXLqxi6RjqVoFypMVnIk0zG9+3ZXA2DeYjBvIMiiLSbS9wr15BuPs5mMUmlAkZXJHfYZ8JoEbhxvvXcZp9O4GNtwfRsnHPnQMn7fOV7F3jfWxc4c0ZgZARipsvMTIa1lTqFQgHH8xCFHpc24O67EzguzAQmL/bm8Syf0dHQnnl6qgA7FjY7G/fdbg9dU1lZ24wyxsPqOraYJ/CDq+pg7cbhhYM89/xpfvM3f4NSscjq2jqiKLG6utbXM2vwm7/5P7C02ULTfVRF4WMf+xW+973H+NjHfoWNjQ0mJiYoFYt875FHuPXOt1KvOpimi7ebmvcqxuvB4wYQiAoXzTHmgLnGCrBCb/QnQBDp9SzanS4pQ0NWVIJuhxlq0J+dW9wSOXigRKM1xhNnGyhSk3tuGWdrq4pl20xNjNGzHdKZFI1mm5mpAl7fenZieoxKORy66nZNHMfBFSXi/SZ6IhGn0WyRTiWIxwxSyQR2f45jaXkdWZLY2ChFZbFqNSyZ+b6P2eyRJEEum47USOv1JhMTY6yuDTKfNLlchhzQaLT2HbraifnJOV6svoBrdNDa4aT6TpjxcKXdaRvcdPwwi5dXw6yjH8AajRZm12RkNMtWuQqEsy5/G1vH61794rIClz99+hz/5s5bWNrs8uwTcQxjjHe/O6BZshlQIiUBsloaBBACYU8A+bq1su+AIYT6Yd+wVni/MY8oCBSc02wqN9ESx9GVMGsZeH602nsnhs2eFQWPpeV1JElkQQVFkfH6bJ1BzyTwfVo9nzcfzfLE2QZB4OM4TvS+njvwFolH0jMDDAKH54VimoauYTsuiUQcx/PZWt2IyBTXC2+3SvCOLLDZaBHTW+iTAaIgUNxwSKdSyLLMifHwnNH0BA8+CCItbr01zVLJ4s7bbgIgHruMpil4nkJxs0MylWB9c4tUIk6+ME1WFihuBWSSHfQ5jxP5Fq7tkp6TQlqvZZHJpIjpOqIkoulhX61aa4SClKJALpumXAlLrF5sGtHp4ksGicZTwPag5tUQGmndBARMTIwxMTFGz2xFC6p3vuNuAJbPnKftarScvsFW7ACPPBtS088Ww/+F9CGePV0kobgsFEZQX0M9j9eDxw0imUiwslbE0o6S9H3GAwu9/By93izTozrFhkcaqMduwnVscskGtTJM5U8jdMDstrl7QeaHRYmHnt4kqXkcyOvYjku93iA/kqXTNVnbKCEKoSPc6tom+T6lsdM1MV0PPA+73mDQTpb6su6JmEGz1cZ1PWIxPeTVux66ppJMxikWK/i+Ry6XoWfZuLhcqDaRmqEHxdTEWORxnk4lh25Arhe+T6lcjUpa14In7aW1Dhrqjm5SrzeJxwyymRS9nsXGRimSom8vD1hNIs+XXR5T1yLtnzQK43qaZtdCVbf3w1AU6PcoTj2f4PSt3+R/nLmbM2cURmIdIGS7eINh8SBkRyUSCWJ9z47/j703DZIkPe/7fnlnVmXdXd3Vx3TP7MzuzGIXWCywBLAguAQvECQhCqRJm1TIQTssKeQIR9gRtkwpQkH6YoTsD4LkD7Z5WWIEadMkDjJIiiLAXZLg4iIWe2OPufuu7rqrsirvTH94s7K7Z3pmZyhSwFL7RExMd3UdWVmV7/M+z/M/ru/s8mn3ym1Vxzx8Yj7jXuXv2zGjeJVd5SJki+rcI+L4fGp37wBSKBYtBsMxmiJz4+ZOvusPwwj041LwSi7waBoGMEHLzrWZzW+ELlpEFEO1UspbXp7n59whZ+rS6w1yFFacpMRxzGw2pVwuoWf6WvcTVsEiiRMGgxFeEFAsWNjFAkmnd0KhOE5SNE0lCEJkRabXHTB2psgyfPwH1wCZhcaAVnOdJHC4ud1heanJZia9Q5wwGovqxpm5NKIpfgS2FdE+GNJMFboHPULfx3jAoLlw+obGMA2azTqOM0PTtNx3xS5YxGYNbfQmkrGQJw2z89XbEsitIo3AXa2o5/FW7a+3e7yTPO4zglJMiInmuFQXG/gB0PwQLaeL5W4Bq/l9Pc8XcJIsJLfDmgJypPCdVZc3jTU2BxqHnbO0llNUo8Fssk+aplQrJeH7UCqyP55mVq4+jaJBOnFRdTOHNMZxguNMse0iO7ttVFWhlZEBFUXB98P8yy92qOIir1RKXOlM0J0ARVOFSGP2/9wr3XW9rM22cpQs0pTRaCx8RJwpjUbtRCJpH3Tna/TpbassnHBERauh64LjoCqK8APPjm++mF5YSPjU7FXSY2v5jJhfaPwEcmXvNt+IR0uv4XkOVy7/Gd4Vl1966hk+bqyzJO/TT9YypXX5yBgqkZhmFd1csiRIT08c8wiJ+OWJzCcKFoqU5K2iOSNwTiSUZfEZ6JpKtVpmrzvmel9mjs2VSDifIVODzAEyimLhMxInTDOto2dfG/DedSPnCYVhdORJoqoMh4IgWq9V8mOxssXNdT0s06BQMOn1B5CxUaRMFv5+QpElkjjBtgs0zBq+59PtDfFjmXH/iFGtyhKyqtBabOaLbGOhxu7eQb7wqqrgV+zttbEsk8NuXyhH6zoz16ORGZrFcYzv+TjOjDCOaS0toHW28Ny3htPu7x+yvNSkWi2zs7OfEXQtKlWRcINwkUbliNjrNT54QpoehF/LrdE+7FEoWPkxHvchuZtQ5L0knbdLvJM87jN0U2Y2gULByr8EXhDhRTpS7T2cnV4jnQm5h6oCaj9gSQb6EKHzhnKBVqbQWk26nHs0AHWKphXxghS1UCQeOyiqSmc45sALqCsSTWVKIhnsxDMKckHsGFOX9uE0b3GMJ0LJ1zTNXLG3ULAIbvG1mLc4Dg+7yKqOlcActXr9m9v5/ZYXG0RxgjOZ5agsgGr1qJd+/KKZTKakbS8SKQAAIABJREFUhkxf7uf4lDvOPbLwPB9NU5kW+lTDJdJMUmQesizjJAHPxyfROCEJvz95mU9aCwQHB9RWV5hmu//2yGLiX2ckCYrwZ4Mb/FDpQv58CQkLJZVEChg7IvEospyLJvppxKpsoygpYWiiGwlTN8TUVWRJIYqEflKiSrlGlSTPk4GEJEGQC/4JscbljbO8fvOQ/bHKxaWjysQJNa72Y25u/QAf+1jK1uYmqytLORS33mgSfk1Hb75JkqaMx86JHb6iyPR6g3xelSTHEhnkBMC58mytVmU4HOH5/qmeJ28VQRhRKhnsbgkk4cb6CqurS8S9PoWlJpIsIR+TsT/u5oisoCrqicVzMBAJZ77gxnFCsVigVimz2xZil7qi0u+N8DKE0o2bOzx0rBBoRkcLvTOd4UymmKbBYDhm48yK8E2JhIXCUrPBza1d5KFMEATomnbyDcoS2uTKieRx/Lnneltz5Jrv+SIpf+U3aTz5UwC4rk+pdPvS6gfRifeeJqkgBs9cfD8QskKnSdl8m8Y7yeM+o99R0ByH4JgGzUH7UEhyByGLixez4fJMXMjFIorv4ReKFFWFkuvRcxwajRqRYjNwVGAEjFgKvskfbJ8FZK52R1xqSSSeS2pZ7M9klks+D1TWGaZHIm5xPBYXpKGhaQoTZ8bEmeWVwtw/ZG5ANR47KLKEYRosLy9ytT9GRZgUtQ97bFxaZW//gJXlJQ4Pu1QqZepmhc7ugNWqlg9pp9MZrVZTQD5HE5oLdXbDmxDmrqOAqDyUUFygUqTflkh6yj4VqYntNkAROk16Vv3Md++/PHuN0+L/GX2V75G+l3K5xGB3j/UzFdqByUBv8px1tAtOgN/YmvBh7wd48sMph/uXKas2sjGjVnZBu8DuzjZ+Jsj348UH+dvxeTbWVzBMm0n7dcrhLt2C4DG0OwM2+zLM4DIJD688yMpKwO6OmJPo6yr+Zpgn70r1DK+/LrG6qtOwxJBazLMSFNfD1hIe+nDKxIFXd0M63RLvf7/Nl17rMU66nLngcelsK/88QcjB12sVDjpdLNPg8U9fQtjnwI9194Ei//Rzj5xy1lJ+5wPPUCn2yZ1n/+IuX/hbwnFEC0dRZMp2kclkynjisJgklEqi8rWyOYqiKniej1Wv0e0P+PGvfPie1IMtJeLzH3uJeq2WKzm2RxLbNy9w5oGrNBcjpKSHaRgkx/TP5sfyqU/9C37hF/5nrly9zhe+8AUuXrzIpYsXqTfqvPzq62iaxqVLl3jhhRd4/PHHefbZZ9na2uI/+vEfBWBgPYJ8SoVwmlDj8fvME8itc6H8vrcM5CVZsPiPt8SU+wQwfCvjneRxHzFnQx//0m5u7VGvVRiNJ9jZxTRxZqSlEsxmLBYt7GrCtd0xHmLnWynb9HoD6nWLUlHluecv8+CFB7gZn+fDFy2uH3gcDDx2BxJFy+CB88t5O8SZjhkNx7lsRJqmeL6PaYpSv1K2mc5c9vcPMTIP9MFwlLcw5CxxHHdSOzzsYZgG1UqJ2czlzNoyURhRr1Xo9AbomkbrXDNPOP2sPTFX421qGldGr584V0l8tKNL5GzOcQp0Vwk11FglSjzISv35Lj6OE2aEt1Ud8whJ+CNvix/WN6jXa2xtD6jXJPpFi189+DOeIoMrE/FF++v8ow+c5fmvV1lpwTd3drEqFqZdp1HQ2O4F+AmEkUyKaCtd7bb5yAUVCsu82ZbAFYNWTYb3nNFJkpTLBxGSOQSq2HaR8cQhdjMfjzjh5S0faesyP/Thh+h2fDwvyJ0OFVnO3SGl7T/GUS7xgQdLvP4GfP7zEkurGu2+SLYbno9dtGg0anz+t19GVZVcs0yWJX5s1MvPy5onEsyP7f/uqefthT8d8k0t4W/99Oqpf79b5BWWJOEHQkByrgwNYk5WLFjstQ9RY7EJmLkufhDes+y8G6tEYcR06rJUXODq1SYvvXKGOJY53Gty7uIbOGddWjYkko/qbBOlDRxnmi++tm3zwe94Hw9eeID/6xd/mZWVFdY3NvjABz7ALNOWevxxYcv89NNP8/M///M5gkyZ7TKSl2jepb2kHNshRUFE48mfot8f0u8P0W6tZu4Sfxmb3W+XePse+bcgEoR5zXFkzsb6So4jLxRM7GKB0LZRZQlTlrC1jM0ry2iaKtjlkljkZ9MEZwYbH1wDoIiFMt3m/aWrfPd7VyhaBvXCOaauwh//iY6s6RimwcbZNTpJj+ubWyy3FsXwL44pWCaKogjNqEpZaBPJEnaxkJMCgzDKyY3KeEwcJwRhyGQyxfV8ptOZgPMe86CI4xhv6OZD2Ykzo16v5miuNElQlZMXjKyEJ/6dej5jwQXpKfsZFFlUHfMSXpYlfnH66p0/EAk+G1zDUwTirFgUhMsXwy2iW2YWMTG/d/OPWFp6nTgVPIAbbZmrNx1efEHiTLNKqyhxfkHMWD52Zo/3rht0JnMzrwr1WiWf7fiB6HN/4FKD7m6VkTthPHawiwU2SmdptZpc7cq0KgrnF+Dmjet0ewM838cPAkxDP+FzAUKyIkkSPvAdEx57zy4F5ainPxyOmEymTDOU1Qly732iO3XtL98aieNYfOapaLnMpdXTUhEmMwH/zc5RrVJCURUGowmNRvVuT3tbDIZjgkDlT764kSUOBZBIEoWbVy5x+eXvZBTazJIKqivaxEEoNNx+/uf+KWHg5Uiwn/7pn6ZcKnHt2lV8z6Hf73Pt6hV2dra5du0q//hn/1vGw16+KSpLQ8r2UZWR3snxMUnZ2dojzjaS9Xo1v85ujeHgdPHEt3O8U3ncR8iSxLTbO+G/AVAsFrAKBfqTKf0w5ny1JFA3FRvX81H8LqZhEkbZIjqDUeigaCrtmQe+BqSgpxSVBrN0Fd0PWGsYfPkrcOaMzMX3jJhMS8xVEnb7Mz68cYbrnavovs1GrcqElMAPmHAE09zc2qFaqbA/noqZgKZxriKkRVRFxtGnLC+XiCKd/mAkWkZJwng0wSpYqIpCpVJGmiYE2UXSWmzkjof5ULvyEG/0v3nqebuVMKhPSgSlyYmkEoQRpiEk53f3hNbROPJ5Prk7ecpPY55xd/k7dp0kSQjSmF/rvUJwC1LKSyN+Zdbjn1sxOwMVkHlkVcX1oWjeJElTTENmSR9RloZc8TcwzybE1xN6vYHwPM968gAkCdNMlr1UOmRrW6M9SuFgyg8vCUjqeiXGLsiAmkOf4zihUi4RhiGKRK5lxnQn34QAOdIIxFA9jo/ADh/7yffQ6RxVGs1mg//xD9+X/z6vOD63/LdPPWf/w49//q7n9G6RJgm1SolBpmgwn7UcP92zmUiKYWbzWynbuPcw3D4egVflpa99kDhSSdOTgIg4UjjYK/L0H7yfR5/4Cq1FiAz9VO7RdOqiKDJLzToHnT6Hhz1cz0dVFHzfz1tsw5FDFB/NZ8aTaV55nAYqiJMYVVZZu+U1R6NJDkgBkTT8ICQIA6augFEv1Ku3nY9qba5i9g7P429kpImYDYRhRHAMgTEfGjtByPmimsM1QfREO4eHeCzlntdxkojqxHPRXJeCZRIEIfqGStkRmklJkrDSavJDH5+IPrenMRz2eaUvvuDf975LyPE2X3Ym/Mz5h9i8uUOj2cCZuaxlDOKd3Tat1hLbjosdzLjQaubcjUrZRpJl0l5K1/dYWhLJ5lZCm2Uagp0ce/lFYZgGw6HQZ5o4M3r9EUtLjXy2EWshqqLRKDSpaaJ11O8PKZdsVE3lDU4mGVXRaDRqBL7P7t4BURQzGjv8unTtnj6XT/vX+JHZA+i6xpe9A5I7AKX8FD493ufvLqwjyxKbvV0urTRwZhrNQkBZ3mWbhxixSJJ4sC8+R0HKDIgz4l+SJCiaiut6eTVZliOqSzJn1tcJw5DueIR6bIYzTxwgfNABkOX857pOXols7+xnf5bJjWqTJJ93zP82j17vSBrmXmKvIypVgcS7v4F5nKSMM1WA+fuSJImxM2OmDCGrZkHwheI4wfPvL3EAqPI6SXx74phHmiqkCZjaBjFXGI0nNBZqt91vDi7Y3NlnY30lP4dLzQZjx6Fer7K7e4BlGUKzLou3gqHvtTu3ESznhNB5hQ7Hk8LJMHIknE9l+gp0IdVKb6PU8U7b6r5CVhS6vT66riHL8omLeeT5zIIISZZOfHkAmmcu0WjUSNIUWVYIkdAch0qlLPy0laNKZpCR8FRVoRN2mEymbKyv0Go1ceWAB+oRZ+tC7sTjEj999ikMs0SpbOM4U5IkodPp5V7Z246L5gi9KxBDv+ZCnVJJMJGr1TJLSw02t/aQJXKi4NzRcDQaMxiOae91qFTKDIdjdnbbBEEohvEFizAK8T2fB5cewk6qXKo/woXKQ3niAFHSz/kjAMY4k8iYlFBGBUajMWEYI0sSuqaSqhJfc46ZFN0lYhK+4G4SkfC78fYd+RkBCX+uXiNIRY9+tdnEKhQ5r91gEtncTC4IHahbEC+KLGTLC5nMiZzNKkD0/iVJwtAFEWxvb5cbN24wOiY3YVkmhYz9f/w5VVVF1zTqtZOtjiRJsQuW8B5BxtYTGs0GG+srlOwCrcUGdsESsFZDz7gg9x6tpQVWV5aoVY782O81apVSzmZfXl4U/J9jA+K5XH3BMtFULWe1G5na773G6vqI9C0MvdNUYmU9U/xNUza39tjc2hNmZ0HE7u4BiiKqo1tff7d9mLeY0jRh5nonht/H20/JKW2rlVaTjfUVNtZXqNcqbKyv5Buv4wPwza29Ex7rJ45hTyQtOY0IFp4grFzk7SQ1+E7lcR+RpinFYhHTNOh0e7lY387IIR6N87K5PxjlMtaVsk21otHrDUiShEiOiS0LLcjkzjNjpzhJwJUol2whSeKPebjw4Al72o31FeK2aFcsXIQXn5fp9UBfvMLj56u06maOIx8Ox5TrJbxExiqLHZUzdZEkidFojGWZJEnCZDJFkuVc7wiEMZUf+Bi6QbFYQNPAsgwURcbzAyzDOHWXB7wl1HBzaw/F0PDLIwynQqxGyBHUqhXGEwfPD5ALMl+Y3a6ce2t8/KtLSICBjCTP+GOu8NFkIX9Uq2/m95uHBHxZu8FSdq8YhY98/xKqMj5xMVgGkIToagBJCHGArcEZ66hdtCtdZDG9gZsWQC/iRh6gY6knd/P1UFA5d6Q1CpZJnCSYhi5UeGVZqOyq5JVpEIpNiF0soI9DTDXNSYKVitjJVmsVGhn6Stfu7zKO4yQb7Kr3LU8SxUIeRVFkokAkikrZxpxOqbcW2WwfYpkGURRRLFj0BiNcz39LVYJbwzRj7NKEyahyx/tU6y6mGcNEzKDCJOJMa4lOb0CnF6KqKp7vc3DQI4qifBGvlO0TlrOtxaYY6s8X+eaHMDtf5U13ndZi4zb1gXsN1/XuqAqcxEkuSx/rR9eSdJ+kzW9lvJM87icygcHReELJLhLFCa9f26JmF3DsAVc6Dg2jKfqpxTEPVAXDdHSwTZJImIaOLCvohs5ys8ZwOKa12GA8mbJYr9I3BkRpiqwrzPyEr7z5Cq4b8APv/w4OD7uYJYtoDWbXfR46C2myi/ouja+9CS9cG6IpEk89Jr6Q0zCiUqhQ0xXBZi5olIsWUUYui+MYVVHoD0ZUyjZxFKMbek4KtCxTsJLjmFLJhpq4gFpLC7QPupl8u5iRzGVPNrd2aC4s8Frn8onT9q7mQ/nP9VqF9sxBCTVca4apm8LqdLeLrMnIBRlCmMXBPV2yKbAm2Vi6QTfxMCMNN7mzoq8hqXT6Q0rVpVwYcZKWMOSTO9MgCMWikWgokgq2xQxQF+v4m5l512iCtfAuOt0+FgaSbmMaYnY0l98HGEZiJ2qZCjPXw86Y7POht2WZkB2ylFU5vh+gKApBlKIUTcaTKWYYkSQJw6w6nUx8DEOnUbu/YfTe/gGaphFMXYLg/tpW7YMj5NtchHA6zSrtazcol8Sczy5YjB2h96aqCuNTZFruFptbeywsa8ycd2XD8pOhqDHnH8ok8QloNmq8eKXNhbNGXnXrmlARSEkxdB0ng7DPZzLziJOYKI4ZHPbyxT7Wa2w0hZTPrVa6cBJtdSv7fB7zahSOkIkgKg5ZklheXkR1tgkrF/P73W8l+K2Md5LHfUQqSYSxEGgObRvFcTBlGUWWsZwaM3PAYbQHFuiIL06nO0BHMI6jKMI3NZT+gF4Q4GRS57OZy0LaZn3zJZg5LJklblYXGcZlCgiWrGWZqJKKMdZorjfY2d7MILomj65qhDG82Q55+vk2H3m0gWwYjLpdhmlKrVommYWgW6iaSne3zUKjJtpmsiScBCXhgd1o1KhklcpgNCGKIpzpjIZ+tFNrLtRJk6wSSmQUORFkKatAoWBS7NpMLdEXr+lHC9v2zj5n1pbpJCbSQEbXZArLolWgNSDsJZi2DgaUUlPQX+4S//ZDYrC+1N5m/Q+v8alPfYrf7n+T/3PnKwQkecUxv5+awH/eeIwX//lv8lM/+d/kz1MoKJAetaLakw51Qxx3nIKUpshZCyXqx4RSRJqmAn0lS5iGga5rOTFQ11QqlTIz1xPVaubI53o+9Vol53gEgWC0B2EI+tGuM8r8XeaD2jj0CEIBDU4za9sgjJjOXKIoJozu5Gx4epxZWyYMIiRE++Z+Ym2lRS+rqjVNFTLEWetOHEuMrms4zpSNs2scHgqASXqnQdQdorXYIAoP2bx8GlcF0kRi7ewRl8f1fD747g2iQGxk5v4hm1t7xHPzq+w8HR9oAzjOjEq5hH6stRVWLkKSUq9XTxUCPej28srhTpGkQgpyc2uP5kKdKIoIw5jVlSUODnqnPuZ+K8FvZbyTPO4n0pTYtokBZTxG01Q8q4DveyiKTIMWgRsycsewAMG4i2UViR0HSZLwTAvNdXMLIkWRGRzsc/Hy76GHzpHF66TNx3s3GKomv/KuD/Ng9mWPwpAkSXL/jyRJ8X0fVVUpmCoffazGN97Y59lXe1QsmScePnJrc2UFN0NcmZqK40zx/ICFRo3FZoPpzKVYsIS8iaJQLtukSUKzUUPVNGadGYc3u1hmJm/eG+R6W6PBmEqtjInOpO2IKmR3j6nuEIwiZppHEAQkSSoYxF4RT/MpbBjErlggQ6GaAUDgRARKgIGCn0Fub63mU4AkRZYlZrHP8vIyW+1dfrX79dxh8NaIZPiNwTd5WBFPpqoqcRTjugngE2Tbf9ssEkVxNudQmbkeiicMvEzDQI5kXNdD1zRmMw/Pd/F8UQWMxw6KqrCz20ZRxGekGwaOM8X3g1zqolyy8YOTu3H3WG/8aBGR8SKxMCdJkvff+4MRa4dZFXDYAR6/w5f29oifeykfdq4BBSlklr41N6EghRSvXOf0ffYpr9PtcbJZ9eF7Pkb11Tc4C+yXb9CZtm77e7PYRn/tG8TAFIsSN4gRbckYqAM7rkeStwhlkKQcuJCDFhBJO+j1WVpcoNcd0FioCcFETcB1Df32c7O6soTi9bi2V+LqG+usbFyhXBUV2AX95sn76hZqQSSfuWzJhnwFOkJ6fncqYOmyJL1TefxNDUmSsFyX1dUlOnHEzPXQHAejYDFVVHDFYlCxysTDiM3IQdcDtHiGa9to7pQkEexg2y6g6xrNr/02hj9CvqVJoyYRtWDKf/nqlxl86GFShJpnu90hTVOiKMoWkwRX92hQZb99yHJFZrQ35X3veoSdw/nu5qjENlFALxEDmlpk5MH55gJffPlFPvRwi0bjqP+aJCmGKdoAiZWy2BLl+85uG6Omo9cNDF2jH4wZxQ5rqy0Ob3bZ7uyBolN0RQUyHQecry4DIgEuLTXoqOLY0llCMIqwlk38wwC1oRA4ET+SnOfHH3qY1172OFORsSyZMAy4UHVJG+/hC1/f5HzLYDbqsvKBJdTv1Pi17W/gJXffhfvEvPe/+klAVFDt9lEbJlJcgkxao5yIhUM+RooTvwtRwvnMSE6l7Fwl2HaR4XCUk0kNQ1SVvd6Aeq2C7weoikKhYJ3gady6YNwKBV+sF9lYEZ/LjZs7nDsrnB6VY+ghbqbwFgNmEWnu+BcFEa7v85kzX6W1tMDu3gFLCw1224dUyja+HxLFEZVKCVmSicKQnVHzxHE3m3Vc1ydNBB9oeXkR3/NRZIWZ61KulHjt9SucP7eOtRXhJm+95BTUGOnxd9PrD/nIEyMmkz26vYFQHY4TDF0jTVPS5iOoqnqqmCGIxLi5tXO7JH4mM7Kz20ZThTJDHCf0+sNcL04fHplzFTNmuRzO0IcvE6MTVS7x2rWLvPgXq8SxRO9gifd+YJf3NH//xLH4QUQx2EcavZm3p2493uP1yzsM87+hIUlCujuJE6IoyvWjfN+nWlSgYOFMZ0JrSpbRdYHQ6XSnlHWfGBlZFtDFIDTRu9fRw+ltiWMeCimF2EUODtmX6qJXK0msLNfY2xcXU71W5cZ4TJ8UZIUb22MurLd4+muncy5Oi9dvtPnEU+/loL2LlC0CsiyTkh7txI59qedmUJPOgE42+K9WK2xu7TF1pxRlIfHe9QYn5h2GaRCFEbOZR6ylKJZE4oNeyZzeymIRDQlQYpWtrQ7lQoOtcQJjKJkmoGJ5AhFmSCEzhO93EETEisRyUmA3dU6tPWRgRSoyCVwwRf9+7gLoJj5NYwnPD/D9gCT7TMJj2kyVsp1Zs0YCOGAaQsRQUUmypGXoOlEUYRg6s5krEuq0S3cwQEa4RSbOLB/CHk8Sx8+xJEmkaUrJlEmDKXA6QGHuTfE7H3gmv21j/SEUZ4ufs18njhPa7Q6LzQaaLqC1k0npmBGYR7Fg4Xs+P/m1j9yzfMiXf+wK49EETVFw4jibfYmqStM0ZEUmyeTz3/Xwgzz52Qt495g4/vB7vsHe7gTdNNjdOyBNU7QMnbbSqrN/0CEIQsZjB98POJc97dzhE8hNyxTl9tdMIkF0XG0touoqznSGO/Oo149as4li5T+XSkXMzlcJqu/Ba34I31P40tPn6BwU83lMHMNLf7HM1sJP8dQPbGOYWWssiYnsM2xv7/Ng8FXg3mTf3w7x9klz3xYhLoaZFwhEDMIfIE1T1MzP+Ui6XKCTRorKhdUaqqIwnU5RFJV6rYLjTCkfvIpyl+EugJbGJG98iTgRMtitpQUUNUHTNOxigU63S8ELMAOflcwj4uqWQPd84ilhWlUpmjx66WTp/4NPvotPPPVeaiXxmC+9cI1qtYxtF/MLUJIkGk0DZ+pSKhVvgyBPk5g4TrB3v0in02NttcWZ2nI+EDaNo3MyGk3Y3TtA1VQhaSKLHV4URqgNBX8QYNQsol5M0S7gTl3qpsXITfjoY4tcXALHi7jel/nmXoxEwmA4olgsAjJhGPK3iht8j7mGcYcduIHEU9oKnzTP52KSSZqSpim1gpCEn98+byPI2SJuWhLOxMn0tmQBJ05TwijKUVPuzGXmegRhJDYXEmxt76HWFWSE06FiZRIxuo6iyLeJGJbsAiW7wOrKEoauU6/oJzSVzp0VagTH2y5AXoWW7ALtdgd3NmNza4+d7T2iOOYggw6rmaxNu92hNxgRRmGuV3Y/8iHtdgdZVTBMg9bSgoBiqyr9/pCDTJF5NB5z4+YOu7sHeMm9SXbMIoUkTYkToXxQLFi0mgusra+w0KiyvdsmioRRVr1epd4Q36MkTqhWyiDNGCuTvKVatm9vsiVpSq1SYrd9yDhz0EySlP22OG6z81WC+pEfu9n5KnvKJRKtwLBv8nu//QiH+zZxdHKQH8UavU6V3/vtRxhmSL85yvfMmWWuBmdPTRxz1NrbLd5JHvcV4kL3M2Lf6upSrmJqF026YUw1gyR6vo+nGxR9Dzn1WbACHloRi8D2zh5BGJHOhnd8peOhhjOaqURLUUWbIi0gITgaerVGLYMdCuMk8oQwD8dLefWNdp5MLqy3+KOvCLHBRk1cXB987Cz9/jA3E9J1XZTQaQE7Q2nN4cdA7lYoZ4RIu1jId9GNhRqj8eSohaHIeJ7Yqc+x+FG2Sy1sGDhXXQorx5RW98TrbB6YPLBSyolo73vA5vGzJhIJmppgWRayLDGdTgVsOpX5jHcF9w6VnEvK54LrJ+TW534WnudjmgYpaV5ZSZLQoJIkiUAIk52oRJzpLOOlaMJeNUMuVco2BcvELtmgQ+ym6FWd8u6zxO4tSrPpyZ56EEb53/0gwPfCUwlrlXKJ4XDMQqPGxvpKDruduR6aqmLpkuCD2EVMw2BttSV8zDO+T5wkJEnM2vKSSFh3QAzdKaIgxJlMmc08ZjOPyWRKFEXU69W8TRQE0e2qtfcQ05mYCwqobcDYcdjZbeO6PoWM3xJmFazijrjqb3DY7TOeOPR7AbOBz3Qm5HTKlRJRfLIODSOBrmotNvD8gOFgRNG28pZRolgonmirzltM8w1Vr1sgjiWS5PSlM0lk4kii3xXX+rw96QeR+F6dwhl5u+pbvdO2uo9I05S1lUWQBdRvc1O49MVhxM5umzONmvDdsAVaqRSGyJrG1PNJjQoFOSAMPYrFItVKCaVYA/d01MXx0AoV5v5wa7pB3O4RhD43bu4Ql8tYMrQWF+i4Pg+UDDavu3z3+9/Dl164diyR6PnvvcGUWqnEl164hmXIfOKp9/L7X3yRh1pWjiopFMwTcuvtwy4bZ2qi164omKZBuVTEeeFzAMhXPw9NIUl9vEIxstbDUrNBEEY5emW+QEa9GFnLuCxuymBvRP1CjW++EtGqquANsDLtrtHYQVFkFo2LVJf6KFEdofjwPHaxwOfGl/Hu4G8+j4A49yAvl+wjEcYoyn3c2wcdpGz3m2bJY67hNB9Yu1mVEicJSRDmTGZFkZlOXaIMURWXy7iHLvaGuNTs3S8SPvD9eL6PJEkUi6LVOR9LzRew4XCMZRjsDaboLfF9GmWSIJIsZ6ZPQslZkmWq1QruzMUqWCIhBsJzYs65mB/bZDLNh/aqqtLuCA5EdPjW38PjoRpgvBcKAAAgAElEQVS6kCkZjmm1mgKV55ysTMMwZHSKF8ZbhaaqLC812d5ts9paJE5i0iSl2xuI4TcSsgz9wZAL+k1WVz5Imqb5IjyZTCnVjyrl8Wh8UnMqTalWKkxnLpWyTac3YDJ2cg+coP4YJCmK18Nrfih3e4zjhFTukMRn7nr8x8mLnW4fXVNJ0lSgs2SJ4WCMFwSUSzaFgvlO8vgPIdI05cbmHnaxgKapJEnC8mqLze09iAV8spbEODOX0LaRs8Fq7DgMhmNGmWcEiJZO+MB3oPZuoqZ3LltTRcetP5z/PlRlJimksUJctDlTMNifzBgc9ph6Q6pmhXMN2N+9woVlG10XXI354jtnSYOAWn7j+ox/8+UrVIomV7sym4ODnCsCgCRAAIZh0B+I+cYJCeknfiJnjs9mHqoiM3QchsMHCQK4cCGgufQAve4WvX4/e12N9kEHfaAznU5J9ITpK+JCb767zst/EfLBixU0RaZ9GJxAISmyzPvfn3LQUwndLS5fXmd9HTrdHoGesKQatCMvn3noqcZKvICmgqlM8FMdnyN15P5ghCRBo1FjOJoI8lvGmI79ALUQYlImThJUVSHNEF7FrDU3n02EoZiBmabJdDqDEEzdYgBYiyIzJN/1A8RuSngQZkkoxTINkTwQ7O3pzCVNUhRFIY5iipaoyPr9Yd4Om/MHNE2jZNs5VNc0DaIwJIxiSqp4T87EISWl3x/m3z3fD0jjGFnXM+CEdoL9fq/hej5+ENLpiM9VOqa6sLG+wlJzgUa9ypXr9yYzM49K2abXH0KSoOpq7h0iy3JOQhXAE3Eeur1BPuiG23kXxyG4w+GYOEnY3m3nt1mmwUKjlssK+Z5PnKToaoV2lsCO+5tffdWj371zpZaTF7PzAJzgllRrZaHOsGSeCgN+u8Q9Jw9JkhTgOWA3TdNPSJL0r4Hv5giN/5+lafriKY+LgVeyX7fSNP3R7PZzwG8iUHXPA/9pmqb3x1j69x1pKsyJMrRGrVpmd19wCObciMZCjaST0qyXudofM/JDzpoyil3PBfbsYoEwToia54nNMrI7OHVoniITaEXC8hluZLuTc2FMc7xF2n4FLZoRagUai48wrW4ga6LKkGVZWJM6UzQ/JIwE29bQNWauh66pGREt5MJCgqIavNkGKY0wVQln6uK6Hqaho+kJUaQym7msLi/iTGfEcZK3Uo5LjsRxzNe3BqSSypIBhdqIl65Neez8MusbZ3mzHRHGKe9bX0SOK9QWAlzHoVw+uvBf279JxZKZjCeC26Io+XC5ZBfRNI2DQ4mlxRITp0T9zDUqlTKrK0s4fQfPqfFHk0N8Yp75UJfviS7wf6y9n6vc4KnY52pwNn+tvYwNXSgW2D/GdyhliqrFgsVMnuL3I6FHFkWZ93uKm+lQSZJ07z4VasRnvvdLmYQ5KIpoy8yrjU5vIJSRVYU4jnFdl/5UYT1JUGyduCsE/ZrNev4ZhFm/fDAcUS4dMafdwwOG0xGSJKGpQk6nWi0zmUwxDB03TfMBt6Jp9yUjDvBDf/5dd36fSsSX16/QH46Ey55VuuN9TwvDNFiyLJI0YWe3zYXKhLBykZ3dNnaxINqFc0RazInEkcex9tBwOKbXG+bCm8ej1x2gGzpJnCArMjs7+yiKkrfe1lZbDIdjqsd4Tg882GM0MN+SvHjycE5e33NVgLmwZKVcYjh6eynv3k+99F8Dr99y2z9K0/S92b/bEkcW7rH7/Oix2/9X4FNpmj4IDID/4j6O5VsS8/KyYInWwHQqvC/OrIkeueNMc3w/QMvQKKoKUy8hCkMqZRtFlgmjmE6nx43NXa5f+BHCUo1YPnnxxrJGbNd57d0/QUdKORcnPOA5VL/5m9jX/ojqZIui26U63uLMjac59+r/h0WIkkmNuJ5PpVyiWrEp2UX+JD2kuSgWxSAU0hHibwUMTaJVVUklld72JXRzgbW1dZ79UouXX17BMg1BCIxjgiDkxshBlmRG4xJOEBPERZ5+vs1fXBFtn+97X4tK5TK25vO+8wtsbV0lDAMeWpJ58lKVl79RwSomDLo6z77aY7M9YjJNODyE2UinWRRoJtf1CMIQyxBw4dnMpVAwmYwvc/3aNfb3LjPxEor2BTTF4PHGh3lm1su1rQJintWuU6za2Ac2X1SMnAiYpqlgZzdOopjiOKE3mfAvO89xMBwx64akmswvu6/yK95rhJJQGZDm2lS6du+D5kilYNrIsoyqaiiyjOv5+cJiFwuUbBvLNPD9EEVVAdGWSmdRJo9TwJm6jLKFptvrYxg6URQzHju5GOH8vcxnDnM4sG4YVCplyiUb2y6SpIIrNBc7/KsIN1b/nYbAX3/uRTSjwNefe5Hz5y/wxy/1MEybX//13+DpZ/6URU3MCu8kfw6ALEivw+GY1WVRSafHWpq7ewekSUpjoYaiKEeVRZLeBu09LmUCcObckDQ9HZRxK3nxTnH8NdZWW5RKRc6sLb/l476d4p6ShyRJa8CPAL/yV/GikoCYfC/w6eymXwM++Vfx3H/doWtCL8cqWPmwOI5iisUChUIBu1jAdX2cqcdkPKGUJZJeGuJMZySqShInNJsNZFkmUk0OHv8Zeo98P+7CBSZGnWDpIXbe9QleePffYSYLSWfSlMqbv4vsDY/IhFlIcYDhj9i4/HsEQZiXyNPpjG5vyHA04cNBFVLRallbE8q9TubLUa9XaQ+F4GIhA/Z8/vPQaIDrwp++dMjTz7d59rUBr+yEbN6c8dKNEYuL0ChXKRVlPpoc8PhZkyfXM/RLkjIcjghjn7XVFv1eh1q1TH8w5NLFmwx7W1TKE77vfS26uxVKRZk334RzTTWv4sSu1UJRFdHfD6PMPOlo1/zEqmhpyZrG7wxeJLyFMxGmCb/bf5PmQiM7J9O8ajANncPDLsOh2KGrmdf4n6dt/iTa5Y9mNwD4g/E1nvF2eNrd4veHV9FULWcpx/H9kbp+6A8ew9A1igWLOPMvWcgWwfkCH2e75iCMCCKVTm+aC1m6rod/zAdkcVFwb9ZWW5TLNs50xuFhF0sXagGmaRBGYd6yGmZaU6VSEV1TkTNRx7/MYPtu0en1sTJQyerq3ZnYt8ZHPvIRAM6e3eCNN97gYx/7GABPPvkkP/Uf/ySHXpmL1hYAB+ndWz6u63HQ6d1m8bq02MjbVMdne7dKrJ8WViFidb2HVQhu+3fm3ACrENE+uLuVwN+EuNe21b8A/nvg1vrzFyRJ+jngaeAfp2l6mnykKUnSc0AE/LM0TX8HaADDNM2b/TvA/duafQtiruM0HI5ZWKjT7w/RDZM09BmOxqiKQrFoEYUBpXJ2uhIPw9MJ7ICaUqQXpCyYEhsbVfq9gFLZhvIT7BbP01io8UZnQKtgspQk7IYhrj2gM2hTCyZ35IRIaYIeOqwwoJssEsUxxYJFuWQThIJ3MK+IokB87NV6Az/wefbVHh997wr7+3tcuHCVw3bKdz91BO3tdQ2CMGRxqYjvCSilXbSYTUccHnZp9Poo2bmJ3IjIFIil41Lh1WoZ3xOJxJm62EWLg4MesiLz6CPge/DoIx79wQhD1/PWm6KqdDq9HKI6mUxzNNTe/gEzy+LsYoobefyr9vP4t8yPAmL+78OX+PjZs3BM0mg8nuTIqVq1TKkgs9MeEyUJvzUV2lyf9a7xg9XzfHpwJX/cp93LPCW3sFSNKIru23PajVUa9RrT2YzllRadTg/fD6io5BbCjUaNUqnIzHUBmevdiPXDV7iVZx1f3zzxeyH7J943WFzObwfBvF449rMKnNxj/9XF4q6QlY+ztu7cIvdewn/tdcLxmHoUUQdmL7xAlKR89+Pvpd1u0xyOmE4bsPkS9UfXSMMZoZ/gzPrUagsEM5f2JKBV0iksLjDzPOxigf39o9akmm3ibh1WH1fADeP4VOtZgMeffJOCOYfjJpBwYi4SxzGTyTTn4SjKSedBEJ2Mt+uwHO4heUiS9AngME3Tb0iS9NFjf/onQBvQgV8Cfhb4n055ivU0TfckSXoAeEaSpFeA05p7p66KkiT9A+AfZL/+Upqmv/RWx/zXFUmSClbyQZfW0gI3bu6gqgq6YaJrKivLi4xHE3w/xDA0hsORaCPJFkM/RhmpaA2VojJh5OlMekMKlilMlNKUd4UahWc/x+OTDikprlpmeu4hhrUWS+0bt1Uct4Ych+ib32D5qZ8BYGf/gHKjTEEz8wV7Y32FXlcwnp99tkhk7/HIqoqiOPkuWlHknGQFYo6zubXDzo6AG85mbj7omy8SPP5ubM/nsNtl8ewCztRlcbGB7/l0e8NMlVdhOHIoFEw6nT5pmlC2bXZ229RrVUajMQXLzP3fPS+gWtE5d3YtJ4BJsszW9h6yLKNXVSRX5vLly/xbf5MgPh3ZE6UJv92/wrsLUK/X8P0Au1jIeCKWADOMxUX8tL9NnA3UozThf+8/f0Li3U9jngl2+GHl7KkmQfcSty4Y8+eRZYkkSen1BqIqOHa3raxyEredfLxpiOpibbVFrzugWquguztsjgRHJAwjdF1j4kyFd0wY5d9fu1gQroiWCTf/Um/n1NhaaGAa+lF75j6ee9fQCaoVFho10iQRQ/luj6Yn/FPa1QrL1RhfWoFXr6I88RidfhfTqNKdBIAwMBt4Pk1Fptcb0usNKdmFE0TCwWBEpVw6segfl2W/m9C9pij55yif0sC5m+6Vqv/NwCndS9r7TuBHJUm6iRhwf68kSb+epul+KsIH/hXwgdMenKbpXvb/deBPESI8XaAqSdL8LK4Be3d4/C+lafpE9u9bljjmEcdxDn3cOLOCoeuMhiMM02Bv/5A4STAMDVXTqVYrqFk7YN5b7/UGqKFG3w8IbZtZ4GEGDR574fOUv/FbaMNd1DhAi0PKfo9Lb3yFx174Aro/veMxHQ9bimAiCGLlRhkpgjiZYheFbtXu3gG2XSCSLc6cgQfqEa7rsbk5FIN0Xcv9t4+HZVqYhjCG6g9Gwk3wuZeYNup0zqxmC7KFa1nZYPOIoatnPXnbLlK0C/QHI2auhx+EDEYTVFXl4LCbEcPEuT13di0fbt64uXNko+v7nDu7JnwU9ApRFNE3HD4XXudOOrwBMZ8ZvkqYJjS3d1k77LC4u8/F6Yzp1M1bOun2Np91r+ZoLJ+Yv/D2T0jDR6R8zrtKREoQhMTx/ff2O90+o7HgLvhBSJq1wM6sLWMYOmurLaI4prkg6oRWRaFsF3OCYkLCzBZVnaLIwohrLqMiS/k8RFVVRmNHtBBHk/wzjaJIqNY2amiaiiLLVCo2BfX+xAvvFufOrqHffIbeV34T4J6f21KiI4BEqYgky0LevVgkSVLCuW97YZntnT3StVXi514iiiIq5RK1agXd0JFkKefdzH035qCAuWdNY6H2bbGQJ9nnksS3e8l8O8dbnrk0Tf8Josogqzz+uzRN/64kSctpmu5n84tPAreZTUuSVANmaZr6kiQtIBLR/5amaSpJ0p8AP4FISD8D/O5f1Zv66wpZkiiVJXStxuFhD10XEEdd1+h1B7QWG3R6A/RjpC/LMjG9PTpD0csfjR2SOKFlmURhyChNufjq/4vlTU/lRUuA5TnE8r19yQPZIEZio1al7/jsOANMw8DzBShuY32FZ76xQyqpPFAXC4jjTCmVS0zGE5rNBvvtwxOQ3vn7KJWKORTTMA1iQF5cIBqO8r6x5bqEWS95fzzFSmKax7w/RsMx9VqF2cwjJc1Z6MWMy1Gtlul1B2xu7QlTLE1j/cxRH3o0dphk0tpz3a1XpRnhHcQQ5+FJKVc7fTaW3w2IWYUiwcWDDp0ool4q8Wv6gFh662oikBK+Fu7y1B6YDz/4lve/NTRFwYecOxCf8pIlu4iSncep63NYO4QsHy+++QrexXefuH+SpnQ6/Rxq7EYzFKVEwTKxChaGoSFJEoqqUrRt2u1D0iShXlTZnqVEYcyf/fBrdAdDapUSnazlqMgCbv49f/zB+36fx+NLn7zMzm6bZqNG57CHXRYwcs/zcthzfzASYpWJiqZqONNZZnAmTlAUR8iJOCezqcu7snMf7+zSCgK6vUGOJhTqxcLDo9MbULZFIqpWykJSyPNzGZW5Au+d4jQ47Rc++01kWeJjP/7uf+cENB47oi282PgPRtvqNyRJaiLWtxeBfwggSdITwD9M0/TvIRqdvyhJUoKocv5ZmqavZY//WeA3JUn6X4AXgF/9dziWfy+RJAnbW0OQBM6/Ui4xdX1m0ym2XaTbH6JrGr4f4roepXKJTqeHaUYEUYBXVFGmMkW7wHA4olqtYA+2MfzZXSXtJEBJE2JklLsskrGsEq89zv54xNpqC3MmM3FmaJpCGImFqtMVUNqVakitKox2KpUynuexuLjAaDRmubVIFIYMh2MKO3sonk8B0SdfA7pn1oife4n+xhn84Yhmo8b+/iFedpFrmsrVzoDFiok78IjjRIg2thZJkljAgE3BP/H9MB/eHnR6xK//GwCaj30SVdPodgckiRDcu3FzB1mWhXRIPGP7QPAyUk3mjF5GRmXueBrHoOti4F8sRURxSkfXCKWIWqHMNHSJ3JithQbG7j574zGfKXfwb00ep3wwkQT/2nmDH1l5ivgvcbHP4c3zhWJttQWdLYbDcS6q6Exn+UwmNW9/jsU3X8FZfSo3dpKPyZxIsoxlFtDtGp1OH7toMRyG2HaRaNqnMrtKxYKYGqHyEP/J1x5jeo+IsfuJxpM/lf/se0L9uX3YQ1EEbNgPojxxzOXqAbq9IcWiRa83RJElFvURaFDMOEeepKCEQ7SRQqoU4H3vIX7+ZZobZ0jSlJnnMXZmGMZRi24wnDCbCZRauWTn7andvQMWFk7Cd+eGavNYXVk8cXu/f4SmOp44bn3cvcT+/iG2XWQxs6tN3oLk+u0U9/WNSdP0TxGtJ9I0/d473Oc54O9lP38ZePcd7nedO7S6vl1DTlJWMjOc9MwKQRDgOA5RFBEEAYvNBpIEu3ttFEXFsooUrBmmYVA3q4SjGD8WENT5wtEcXEa5hy+MlCZIik4aR0e+1scilWQSs0JUO4sRBLQPurnE89wcB4BWwruiB/G811E1jUF/wHJrkSCQmc1cJs6Mmeux3Fqk9OobxI06yqOX8t1XHCfUXr/M3lKTJEP9uJ5/QnIjjhOUIKAglSjUhZrs2mqLw8MekiyzuNig3x/mNrzb23skqVhEd6KnUBUFJQjodPvIssSZtWX8IOTc2bWs5SAz9sR7W1kp811bEX//0SO579dvHvLwWXHBd8cOBSXhxsgh9Tw65i41yliKwYQjSfQv3GPVMY9ASvgDDvl4dwjc367cvnId+9jv8f4Bs5JBaXJDIFJ295mDNktmAzt0eVQv0dXFo8LzLXRdw/ADrHhAkw6ymmkQzLtDLmjuHi10/PY2DbsGcYCrFJlUngBEXlRl6a8lcfS6A8bOlJVWE8M0GE+mFJI+Zy1BKKRzk+M5sQK5IVZFFz83LZ0b+kPolWV2dtsUrFbu865JGkkY02zWsXtfw3viQ8TPvYT0+LuxiwWmjktraYHJZEqn08+95D0/ODGrWl1ZIokTet0BRkbITJOUOEnzyk+RFaIoEm2vRPjSC9kRKR+wG6aRJ477SSLNRj1PQKJ99Y6T4N/M0FQxnOv00R0HebdN89YeZb3KQw8/zLO/9csY4R5LBQ2z0YDSJZYe/TDP/Omf8eQHnyC48TKNzcuY9+jTDRCbFaIoxIxmSPERnzJVdEKjxM7Dn8R3xUUzl1SoVcukxZhapYZhSBDDMy/Ckx8qkyYJy61FdjMUynznVyma8MIrzC5eyAURNzL5b1VVWLhwjmVLw3N8kaDSlEajRmc4plqzqK/UuLG1lz/vnAilKgpW5rddr1fZ3TsgiuKsreYThSG6prK4uEC3O8DQNRRF4fCwx2KmGTZHXQEMhyOiyECWxdD8wD5g1V3hxkGK4v3/7L1ZjGTpdd/5u/sSN/aIzMilqrq6q6t6Jdkkm+JOk7QpWKIs21o9MwYMGAMLgxk/D2ADHsCj93nUPAwgQIKkgWfGliXbY1o2qb3VpNjNpshea8mqXCIy9uXu2zx8N25uVd1VlGaGTfUBElmVmXHjxo243/nOOf9lwpXHdhmsFlxr1dh0aqxxP2szpTUSxt3t8a/dtwgfYQC+rj4+++EfF3X3I8T+RhfHtsoKZDZfcsO6y5vWZWzbIkvF3Gw+WxAu4Ea3x63Jkm3zJlUpxVXbJHITq9kgTRzcwm51LVmiKAqt/JCJtC0sjvOUDatDfzAkz+ds9URizdPsvlpLfxURpynONYvAy1CknG3uEEgK70SPYeg6hq6RpCmeH7Dd22C1ckuTqbTigCJDmnHJMnlnskBLxXtlW6Yw15IVDvrHBGGEUX0SOfbgxpOkr3wXPv5hgjBkNlsQhJEgyBo6jXqNewd93MKEbb3AL5arB9oqQ5EM9JNk0NvsIEkCKrw+Rp7lJfR3DbVOkoQ0Sd81kaxcj4ZeYzZd0GjWLvjW/DDHB8njB4zq5V3kq5fL/697qP7BPaJf+x/5gilhmEAWwHDJY5ND3Lt/zEbj47T/5H9D9i/yNd4rcqfJ/Lmf5vDWa1zybiMFSxaxjPLUZ8i3rtNc+exNZxwcDsgKOQ3LNCAFT/HIVhopCl/+WxneKVHWRr1KxbaQvvM9Aes8pkwce3cPsW2L2SmP9qP5MVtGldlsTppmyBIkssJms87qeMVy6ZI6DldbQoZhuXRRVZWKbaEbujBQCgKSJMUyjbISieKEra0NlktXELoKYcK1//T5m7DRqOOuVhiGjmldZfDmDaY2fOwa2HaOrEhc7xpkaUhVhwFgzWxer9/ka7Mp/415A0uR+T33DkGWPvKmL5Az/uM7t7HkzzyUT8WZx0YRP/eHn3g4ZvrNhN/82O8TVp7DXetSKRqOEYEBnls4D2Y5jbrD/mGfVkPIl5iWxWQ8YTKZkec5jUadLM3QdJVVEODo94ei/mXCUgpVYTQi26O18hjlHZapjmUY+EFAGEWkaYaqKAyOR2LWUXGQ4oimlON7LmmWc+iuaHU7VOqb+EsX3w9FJZ1lKLLEeDzDubxN2v8OFcVnv21RTcNys9PttLBtkzAIiQuiLohFfw3VPU8CPB8PU0Xc3T/CLlpwaZLguh5plhGGJ0P70zGbLsoW1XA4wbIthsNJqer8fogPkscPGOfhlnGa0x8c8dTbv4NRUZDOtaKkNKIiJXxq/AeiBfVQDt0nkcsq4ZWPoagqy+ouNzuP06xXmc1XbG62+f7rb5eCi2maslOtkpg6qqYyHk2J05RYyhivPA5ncz7+RAV7cAAzHwdITQO9qKoAappKGIS0e5s4+glGfT132F+Jmc1axmQyneO6HpYpBuuxGxAGIb2NdjkIPa3K2yjk4+s1hzAI8fyArm0xmczQNLEr1TVNJAajSv+Uv/Q6NEVmXHhjX7mS02pKfP3rcP1GzquvSNg2tNs6rn5i+KSEMn/qjvl6csTltMqX8h1iKWdXcQRSTtcYJz6L7MTTo3wPAfUUQNGWVNw04Tc//vv83Ze//Ejvp67rjySBfv74lpzwrz73R5g9HaMYisiyxGg84epju/jHb2A3TOaF2KNpGpjrtkoc4/n+D7RMvfKzr6PN30SJpriphdL7MFma4QUBWeG+2D8eAzJv73+TT5pbKBnUNz5MncJnY3eLLM1wPZ/VyiWKE+x2FeVwzKXtHqPJlK2tLvsHx2Xb62A4oW4L6ZbTw+29u4d4XsA83yKNMnav9nh5+gpPq9dp1k9oaULWZlSaeN2+s8/mRqcEeiyX7iMpC4uq9eS+WHuj27Z5xq+cLAf54pVuNE8S1mAwZj5fYJ7yPH8/xAfJ4weINMuYzRbMFyvqBWokz1Lq7iF6tLqQONax/vmj3rQ5kFaaJN0nkFai59ts1lEkCcPQhPR0TSBr6sUNc/vOvhCYOx7x5FPXxM2BxHf3Y6Q8wXn7Fm67Re3j14V2T6NWtrpEVDjsD0vXurViqrHmdQAL7gGiVa0V32NgWejgLa5fw8xSup0Ww9GE3Z0eo9GUTtdkogfonspoMmNne7PQalJYrjx6G+2S7+EHwt513VI7HesdYaNeJY4lNC3nU5+GqgOf+1zO9w4P2enu8FaROy45NjeTJl8bCVOe/yN4h89aPX7auMpPybv4UcQTly6fEWLMsox0I0Xuy1i2RRAEeH6AZRgkaUaSCtlxS3k4PwxLLqrNH5Ajsg4/U6loFowhKYYFeaEEDGDZNnsFV2e5WJKmKYqiMByO6bSbRFGMoWulo+PDRn8wIs+abG3dIPQCJvtHXDP2sBFs73lYwbZMPD/gS3qDY99C3rp+5hiL+RK7YlGxLebzBdV2i+EqotOooepqKRt/ZXernE/sdFuMR1PsbQM/F/dAMk+p1xwGxyPhytlucvvOPp+4/BFenr/K5nKTdrtRorxAkFVnswVXH9stIfcgYMFZKgQTFUVGU7ULOlin434y+bZtilZV+miQZ1mWiPwY9T7GVT/M8f462x+SSJIEveqQZWLB03WV6dTjMfcW8nuYO/0giSNWqyytj5L/+WuAQDyliwVSp0trZxuymPliWQ67AbZ7XfrHY+I8J/qLNzCTlDRJQN4kl1SOt7dKP2XX86lWHRRFZnenx97dw0IbS1QFuqaSZhkbUcyjiC5cSjMGN2/DJ17Askz2D/pCEC63CPeWSJJUkql2d3rMpnPahWd6FC/LvniapkRhRGpbeJ5f7hDXQ/yXJ69waeoTTCJuTVT+RjbAu3ENgCw96c/dW3l8I7hLVugSxXnKHzHgb7JLrV7DH47oHw/pdjoCDFEIYC5vedRrDtPZHF3TSvCBqsjEiVjcfvcL30SW5VI2Pk2FMZMsy/Ti7zM3ruFnOvOF0JBacxD+MjGdLQqOyhrMKBaio6Nj2ooH6IJVLctCrTfP6RTs9Wq1In6jN6wAACAASURBVOyE248mS2KbBrVig6LIEo5TIWgIg6N0MuNy+ob4QwuC9o9RO7frdio2XhAyns6xWiaBYbFbMQkXCxrnNKWQJeRTd0y70+Tg3kD4lVgmrueTpim6rmHqOlGc0O222Ns/YqfVRWnorJYu9ZqAHqfFjKfRqLGYL5FkmdVqwtbWBkmUoOrqBfHEB1UkD2oviXsq5OjoWBAk71N1nI9Ou1lWUz9SPI8P4v5hGjp6MdBVi4FlspzwV1F4Zkjkik6g1xjvvkj1+kfJc4/DwwXb2zUUucJ8MkOdL+lqOv/iX/zPXLlyhX/0D/8hv/prv8YLL7zAZUUTXgXDIb93LAbXdqXCzTf/A7/0S7/Er/zKr/Azf0/IiVUKg521FEO302I6m7PZbTMYjoWj3p17ZxLHlc9/lr0/+CO2P/MiaX/M4OYtalevYMkKi+GQWJYZ3LxFZ7vHnaVHC6EC6xTObr2NDqPJjNlsQZZlJXdjDT1t1qv0+0OqNaf0AMnynDCKmEznXLm8Ta8Yonfmn+exp3NeH0tsGvCf9p/kWnwCRGgZOpMwIspTfmf1BlEpnJjzr/y3+XxlqxxeR1HCZCqY/6qiFAquUqkmnOe5qEaKmVKaZmUiSNOMes2hYtssFsuSfPhO9Bh1TRFJhF3+qtYHuYT6bpeCnGmaEWUJVtVm1xGKsFZRofWPx7ieXw7ld3d6DHm0ykM6B02O4+SMf8vaKS+JEu7tH5EW3jbXCovXRkOoTaPpJLnMY3WT/mBUQnbHf/pbJcT3fnOuNTpKSNdnJEmKqipnBt5JnDCfrDio3mMz2iSM5tRrDkEYMZnNabUawn8lTYliMU/rn/Mz+fe/9Yp4PUGEaV68q/v78zN/d7/4drbPT/5XH33g79chyRJJkqCq6l8bnsdf23AqNrIik+c5QRCwWK6QJIlY/asZPiabT+K++AscHAyoVCyQJFaL7IxlaU1xcZwDcp5htVrxwgsvcHf/Hj/7s38PQ0v4L3/4Cm+9+Saf/exnefXVV/nn//yf8/LLL/O3furnieOYf/LjP46UZMTf+g5VBMJz/d0AoaN02KeHmIecThxXv/wlbv/n/8LVL3+JLI7Qdrbh5i0Wt/cwn32a3osvAnD7P/8XFMvm6i0hMFgHFpMZd1tNJF1HSiKa9arQ3trSC50woaN1PBzT63WJ4oTZbFEitrrtZrmg+KcWlziWGBUnubvLmZgUQ8tvuLfPsMUBkjzlt6XX+W/5VHGcGNu2SJKUZqNW4vq9U2KEqqoQxfGZ90OSJHRN+GHHSUoYhjiqXcKyF15Eaj/DTnobR/F507/MXzbWsiRrB0njika4F5PnOZNFSN05qwi7roSSOMYPwjMy4w8bi8UK3Ta5N3dR05QEmXlctGlSmEwEu10KAlTAKRj470wWKIGAE5u50D7bP+iTGhphGCEhUSs6k+M//S2Up3+iYMdnJcJuXWmOxlPiRMBlFUXmIDxkU9rgraN3Tk5UByd0cHWR2NygSHAR1KQ678xv84mrL9Co1xiNp1QdmzCMUBSFJEpKYqKmqYRhXFoD5Hn+0GKYkiw9tF/Hugvwfor33xn//xxhkrJyPVRNp14IH2qahucFjFpPUV0dvWvrSqDDHxyprBFe+Zi4sdKMIAhRZ2+wGc/Y1CGbWSTVXSRni3ncoQb8y3/5L7HtCp47x67UuXP7Jl/64hf5+MdeoN3e4LnnnsNdjPnMZ4Raqee5HNsWm902yqZNf7CCYvELw4iNjY7o6RakLCW32UwzGE4Z3LyFd+cO2595kYM/fYnOC8+iKWIB33z+WcxmkzA4aRUNbt5CffJxLMsUr6V/zOWjEyMejoYMtzaxmiYsBVJFVVRkTQNZeIXPvIDZTJAXj49HSLJMb7NDlmUYpoNlQRDmfO5zMDiWaDWjcs4xj8RriPKUf7t6g+CCcGLOf1od86nKWzQRi2nFts5AOZMkxdB1wigqb3IJiSg+sYhdM7Fn82U5H5rOTiTcbMsijhPuBW1kGR63+vxlI8tSFFkmDCN6G22MxCjlRZVoxOnO+97dQxzbYuV75aIM0G08uK9/v6g6NvfmLtpqdRFBNFugKArzxbJMDt1ui+FwwiXbQHFsVp5HGIr7Y91O2ultMC082YPLX6RSsQjD+IwJ07rlOZstStj2Wn+tJ23y7VsPj5f+9q1X+ejjH4FcAF82NtoFbDyGWEbVVb76HhXD//Wrf4Zp6vzEL75QzgTPS7mX8YCh+fs9PkgejxiGqlCvVZl7HrmuE0QxKjl5krD9xIeJBt9Cf4C5U4YEkkyep/cVFcuRwDDJLR1bkml1exwdHTNim2rrOn4QFmzhBWE4pVF3eOutN+lttLl5JGRD6jWfNMu4ffs2kCFLEuPJFFmWiZJDvnXT5ZknZCzVZDIVTNmkUA9d9+v3D/o0e02WwyW6oaOpwgTJ6LXh5i0GN2/BzVsAHP7xN8vzH3z3e2deT/tjH+J4NCEOwpMhdN1hljlcfWyXNM04Ph6xdXAER3C0tSmY8cZJRbF/0CeOE4LQp9GokeU5Sp4XhlUhe3t32Oi2+PPXD7m+W2O+WFF9awidLtQchquEq3WH//XgVZIHABmyPOdWqHBjc4UzcMqFNZrd4//OI/7O5hZ7ezN6G21G4xlJmmJbJkmYomsqkiyj7Egs94TRUq3q4LoeYRQRxwmappKkCXmSo2kqcZzQ57GH/ci9Z+iaiuv5qJrG5/7tjQcM7s+q2lpywp/8faEWbKkJfvIQw34lIVBUemrOzL/4CW40apDlTKZTbt/ZR5PFZ8kyDPqLOWoidu41p0IYhEShEGRcrFalhM3OziYHB4MLMu67O+JemC9WZTW1Fu4czc+2nD739OeI45goC8qk0rW7PHXlKQD+8PU/5K2jd+jp3VIzrdmok+UCCDMYjGnUnXeF6Bqn3AnXM6TTMRxO0HVNAFiKOVSpsl1Efq5/Kclyqev1fogPkscjRpIkVKsVgiCkXTjOHQ/H+HFMfzhhcf2nuPzW76BHS+RTKq+prJEYVYJn/w76X/w2erRCyc7+PrcbrD71D8mNCq0qhFGMYejFYF5mPluUSr1RnGCYbSFxvhJDwbgg2SmyXJTWMoos0Ww2SOKYg8M+G60eeD4+QWl/GsdCkTXLMlzXE/pIq5AsS0V/OYqYL1bs7vTY/vRzmKPv8KZ/md5GGz8IGWVg+h69jQ5EOWpFYzKZsVi6mIZx0s9VZCq2happZWWlKDLzx6+wXHnsHg1IjwbiQ/nxD4PkoapqOcTfu3tYam7NZvMzCS+suPjhiTlQpVXHLUT0wizh356adZyPkIzfmH2fX9362+xt3sHzq1QdB63Z5u8mMA2h0WuAkuA4tqguimu39ja/mrbYS0MkUmGsVLQ3ZFkgd7IsJS3samVZJjF+cLOk8jOTZViGIQbJhwOSZPrw8N9MLWcVv/2zLz3UY+bHGi1T+NWsF24QFqvD0YTLu1vc3T8qZzpKAbRYeT5xdQlBtZxTDcdTepsdxuM5nu+TJGlZUZxPHFkqNkGOU2EynbO/f8TuA4yTPnntkxwvhmzUumgIMIBjOTyx/QQLd45V8Fp2WrvU9RPxzjW01veCd9W5Oh15ll+Yy6wH5aePsXJ9ojg5Ixb6oPiA5/EjGlmWcdQfYhoGnh+gzBd4flAuErFloWkK+x/6B2zmE7I3/wSbiEixGLVukHSfwGro8Df+DvLt10iOj1GTgNRuM2o9xdjYYEu1WClLFnsrLu1uMY2TcuBpWUa5U9o/6DMcTvD8AMPQkZBI0oR6vVbeZKqqCI9uWUZC4rnnnkPTdL72Nbh+HZ68JloFR0fHJX9j7cOxdpwTFpkOTsXGmn+XVaIzUZ+i1dTwgxBV01Bmi3IxmXgBLYStaRwHdDrNkxZYbpOmGauVS6vZYDKd0W03xbkqCv3tHnme01UT+NZ3AOi+8DyeF9Bq1snrkIxiaod9pKKSsVWVaDLFuBxBfAKhlP2Aq90ut2czfvXoL4gfkDjWEeYJvzt9i+cbQg1Z01UODsbs7FSpl3eJhNFQmM3PSqNnWcbt22LuoBh66UUeJ6J3rmkKpil2pkEQsFx5F57/B4ndnV6ZAJIkfWRDpzU8NThtdPKA6CZtct0vRPyE1Md60bRtkyu7WxwUgpphFEGelwZbjUYNTWnTX45Js5yo+P1kMi+NqtaJQ7yWpGwPriXUV67HZDqn3T7rHuh5AXg5hmLwWO8qU19U08cL0be8sSOqjfXPAz/CsZwyccxmC3w/YHOjw3A0JU7eHS15OiRZwjCNE2TVA8KpCOOvH7X4IHk8QqydAzVNpW23WC4W5dxjMp0LYl0YkecZe1mVzou/QKxpYic/maEoCpOxS1M6xLv+JaYdl44WMMvFLkX1/LJllGVCJbXbaTGfL0o12zUqSVVVPD8gyzK6nRYr3UXy7RM4LKIUj+OYVbFYaZootS9dgnv3oFoVHh9Znp/xjljDdD3Px9A1qlUHY/Iq36+16SUbeLM5jiyzXK6oVGxSy+L23UNkYKe4iRbLFRXb4u69QxynQp5l+MEMRRESE+s21nA8RVUUVFUMJMMwYiLryJd2iaKQ7Ve+S/D4FYGQee0NpCAk392FbpPDowHOhkFi5/DmkEAL0BSY6zq/9uu/zj/7Z/+M6M03GS0PudSooqka09mMZqNRfg/CEFVVmM3m/Nlbr/D8F36Mu3f3eebpJ+m0GhwczEiSlCwTlq4nbYXigp1qPcjF/KHfH55RH5svViVEd01Sk3z5obkhDxNXLm+fQT09TKxlX7rJycLtywEr+exxdM8mVFIMHeqOWLxv39mnYlvlop9kKTvbmyVXaHd3i727h5iGge8F5KZBXF0Sq03cqV+i7vwgxLHP7sjTJC2Tx3qz5Pvi86IpCoZpkEQJ/eGIeq2Koih8+PIzZ46RRAkpMJdmuJMAOZRRENXQM1tPMhxN6XaazBcrLNMQgqGIanEwGIt7XZJQZKlEtcmSJDZi5+YX68RxWjDxr0N8kDweKaQShbFaLjEMoQybZRlZluEvlpiGQbNRY7XyOB5NaNRrxHFMq9XAHL7EIrrCsP481dym2bSRZm+w8ovBbCpIT/NjARUNwpC8gLGueRBJmnFwOCjsQ7WSUFVPq6xiv6wclCwjCALiOC0l4sfjY5I4xpUkZMfnojHkyc7dMI0CN98miWMqRMUxpsSOQ5ABFYcQ0FYrVEUhSlNuD5d0bYVckoiTlO0CMQWiTyx8yQWRai1ZEscxeZaTrP0m0pTtjQ537x0yeewyrVt7pLf2kD70BIohzu/oUMA7gyDC8kxCWSLXbRwzYQW8+OKL/PIv/zKLxYJ//I//Ma+99hpf+YmfgCTipZde5itf+QW+9rWv8c1vfpMbN25Qq11j+/ltpggp8jBNMExDXGdDJ0kSAe8EkTBkGbuQkQ/DSLz+OEbXNDTTQJIkPD9AliVsU8iiAwyOR8iyTJqm/OtPi3ZRHIVcM+/yurvLf/3tLz6U1ImtpHheQBRGUK0wuH38no85HwYuSgB+5BG4Pm11xTzvMJdqPNaqsTcYkZsml2yZCKjrVlntyrJMFMfl0Hq92K8VBUAM130/ZLPbRlZknFGbmTxCQSuTqa6p5QZsHek5va0szYqZiJiLTCYzao6DXXA9epsdzsfKExumjUaH7+dvU1OqZXV8GgGlyNKZqmcwGL8rOfDdotVqMJvez+fuRzM+SB6PEHkxe1i5HlWnIvzC/eCM94UvSyQFa1qZLahWKyire0jDWwTdT9IokBmKoggZ9NRja0uU1rPZgiAIkSSJVstGUU1CXySnw8M+um4QxRFORSXPNWRZeBFomlqYGkksF8JcKU0z8izHNPVy5zQcjpFlmevbXV56aZtlE+7cgUu7SybTObIssPWKIjOZzDDNE2LfunUTb8awSLnWOXnNc6XKYrHEsUxyN8WXYsgyVmGEF/pk645RsUtfn0+SCGOtPM+LxTcpfz+bztE0lSiKmFy5RBTHWKuE8d0Z7VYDWVbErnHHZHXsYdQ9Rv0V7R2xSH/WrvD8L/0TYboFPP3pT3NvNsMMQ77w+U/juQs++5lPcu2JJ9CLTUCSJDjqDvc2DzAUlZXrlxXeuupUFPmkAXaOJS7LMkjC+MvzfPE4SUZRFaIoolIYxCuKjGUauF6hhFtAfmVZ5v/81B+TFkrIWZZxa6JyY1PwKWRkthoptYLDEnlVWvkShnCllOn9wkN/niPJ4Z3jAMd26G5dIkbAqeuAurrHs9oBi6SBbj6FgZAvr9gWNiZRFF+YTSRRgh+EbG50Slh1xbbwfJ/FykVVFLKpRG+7g1+grrIsveCHcZ6hPZsvygoExCKdRGJ2tJYgWSs+rJOCqqloBU9nZax4pvlkKX++lhIxDf3C7CTLUm7f2ccyjXJj9ijRaNZYLl1c18Opnmgnh2F0oTJcrdyyAlMUBV1V30fj8g+Sx6OFJKHIMrKssFy5bG50yNIURVXKwS26TuIH7N09FMih4UuCOOVcAigJZq7no6kKpILFum41rawpFRoMRyvyQv1V1zSQIE5iNE2j2RQ7rdlsQaVis1y6AgGkm2iailOxBf9k5VKvOWiaxnA0wbJMup2WWLwsePVVsG147tkqx8MxpmkUiC6bRqOG5wVC/TVd8Rd6MQB0mzhZBHLO0eGqHIRvb/dQFBmvIngvZBm2qiLLGpqm4ssKbhhRzQpJj8snHz1vLySKI1RFtIV0TSNO0zND2b27h6RpRqNeQ1EUMXROE6zcZpGLne4yyDANnWUBlW1VzJLjAYKo9v3X3+adQtn3at1BURW04itVFPrB5L53hVD/1UsGsKLIhFFUSHPDRrfNYf+Y3ma35AsA5JJ4z+M4ZeWesGWiOClnZ+vNh6LI5QC+/MiRkaZC8XgyXzKWWxzoFdJUhTSlYsS4icnVuoOXPhrBLAIyVT1j1jWZzPA8n93dSyTOJfQsxxy+hNf6RKkG8KAhsef7Z5BQSZLg+yHZKW7E1cs7hAUAI8syNFW5sNs/3xby/IDzI2xVV8/wVBqN2hlOS+k9noYURTNpmjKZzsmzjFq9ysGhYKvHccLOziaTyQxJksQmKnpvpYj8ARIzWZZdSDxJFF9AZN2PuZ6/j/w83j90xh+CkCRJEMjSBEWWBeTyeFz2YwPdYLfulItBZfJyybgdDMakacZy5ZaEsnXpPpnOmUrHjKQ+SRoj2TJGSzDXq45Np9sSBjaGQa0qPL/7g5FIWKrLZs8SUtUF+kgt4KOddhPPD0oSWbNeZZHNefmbVXxfJI7r1+GdoyGXd7e4tLvFyvVYLoUybhRFbKoDGuoCGRPcJlSmrBou/b6HYeh4foAfhKxWLkdHx/ieT63qoCoKhqGVLbOuZXCt22Qpy6iaxvf+5J0z13Z3R+wY41gMmeNz8h2KIuP5ohWxTg7NRh0tkak6JzfhYX9YIlYm7skguOZMeXvwNnrnpMWxfyC4FmtorqIqyOrJY7SiygQxIFdVFeOKhmopKAUIQZZlUalNBYt5Np2XFUWmZqRJynLlEYRhuYDmeS7IkcUCdZqAKMsyzrWTGYCji5nWsevTqlex84yGJRKh2KdWuNYSwAYe0qp4HTvdFrudVinLnqUZ3sor5xXihCTucR0tGJY9/fMQ1nXP/zz7nEws2K7no6qK2AzIEmEYkWUZjlMpNgIXJUz6gxH9wYi9u4e0mmeH5OsIg7B8D8/HbLYgiRKCU4S+3d0tqo5dnme9XmVjoy3gwUVVhSTR7XTOQHHvF1/5uef52z//EfIsJ0uz0koWxHvo++GZn51PiD8K8UHl8Qix9n/otBqMJjPGY2FWNC8ITorrQqfOJd4icywGyrPUgbmyZHOzXVQYGpZlYhc6TSCUZeeDFRoOGmB1DYaTI5pSlzzL8T2ffnaPDXlHVBCmgSLLJIpMv++TZT6adjJAv31nX+ySi917EieomloOMD/x4pJ79w55+zgjyCpoSczh8ZisavBYb4PK5GXenl+hpiXcSRo4FZssBUmKwXPIE5lVgTaqNOoEyxXDKKFjmUSzCGyxc2sWO0E/CEkyn1E/YLNiM5rOefbTQntKG2koxIJYJ4kbzzA04iTm9p19ak4Fx7FL6LFpit36yvWIvQXtZYSsqVxbmaBOoWg5z3Udbt4+s2O9UqsyGdxh9eQTXGvVWGoKnifQT7IskEGmvcUy3SOOEg77w5NhaaGgmtxLSeKLyK00DQnCsJwZ1RwbNVOQFAnbMonj9AySZzieCimK9GTOtE4u1tymdbnC3bsjHBMOg4gNxYIAJv6cIJ3T54gNe5sgnRJHBlcubzOb/QD99iwly2VkJGRFplavsiwg1ivXwzR04dXe3WQzeZOY+y/k9wtZkcsKQz6VWBynwnS+pNtpMhiM7wuN7W12WLkeSRyfKNQWseaOtDvNM9Xpad+XRqNGvz8UFYALL09f4RPNF2i1GuWi7lRsyHL2jwbCFbSQPHFdD0WWGY6mZwbmiqIgyRKyJJ85p/vBay3rbIKVzyfWH4H4IHk8SuQ588WSNM1oNetkWcZkOme710Wdv0FF9hm6Dnv+Za5c6hEOxfB1cnNO3hCCbMKwRyxEGxsdmOwLwxhdw245yDGkJFyrXy/Nl3rNLqOVRhIKcpqua+R5Tq/eKSG5mqaVxx2OJqiqSqMhdqSGrrNyPWzLouLYgvF7aZu3j/e51Q/58ke3ubd/hKqY3J6vQHkGLVsxCxWuXO7xztEQWh476iZ+5rM48gupC5jP5li6RrNqE8oZcy8t0DJKuUNdvrpHuPLJ3ZhFnkOvCY8bpH6Okgm9qOXKLW9C3w9K6Y+j+THNqIGuqSRF22HgDFje2eLZeMjqNARyfwXLUxVLVYfdk75zsFwhqyr2628yuPY4hqGVu94sy4myBD3KQYE7d/eFWGRRWXh+QBgExGd2mAByWbnACXEtDCMkSSoTwomSSYYkqWz1NlguV0TxSpgzTe6Ux1kulrirIlklOZaSgKIRuhHosO1cxVFM7t47RNNUjpdjdnY2qVZP+xM+XKzfo3Xrya5YTCZzZEXMnZCkcraTKzbq6h5J0YJdRxiEpFlOHJ9t9ciKfIaFLhz6UvqDEbZlsnf3EF1TydKMwfGorGDCIGQ6X5Kfav/cvrNfyrOvr/FwOKHbbrJ/NGB3p0d07vnXj/1E8wVenl7UoMrSjNF4il4oREhSIUyYJOW5DIcTdEPHqdhFFVQniAOmszlJEqNrOoaho2kalmWcgRgDpS7b/VShT8fK9YWn+imZ9x/2+CB5PELIioKuaWzstBkOJ5iqxNOVfSINFs4NMl2lqqlUnQbD4YSNjTbHx0IC2yrw4FGcEMUxpq6jqAoVIqI4ZmtrQ4gCLuboNfG2XLm8zWQyY5gckKQxeQ7GloY8EwPX+XzJxubagGbtS7AqSYX9wQjLMMiAZqNGHCfkeYBlW9wpEhuID2671SSKIrrKErW+w23fR05T0b6wLCoTC7Wj0tSayHUVs/BtaDXrzGZz+sdj6jWHnYZAIA00naOFS/h7r2P8zl+QFIungoRFjtt/nvwjm4Tlrk4iJy9d/rqdFoPjEW27iWHoLFceWZaxcoYs72zx/K5GK7vMccF0r0503P9wBwowrSTL5FmG89XnWG5JyKpKliRkRbJRi91+pWKXxkSQkYUSVVsnjmNRGUgScZySppng28SxMDFSFcgppTQ0TcxhlkvRioyiuEwcaZqR5XkhqgiKIhYuv2hXJcWit96dRnFCo24Jd8Ako+p6TBIInDnmos6sP8PTVDRNRVM1OsXMYv+gj6U8iGF+Nmw1PTO72NraYDKZ0ajXyt373t19Ws1m2XJJnEvIsYc++Q5e7dlyoVwf4738tyVZQpUFmON8tXGaJ2GYBj3TEGz12ZzlyuPqY7slSipJEkbjWYmyWieT+2lOrVVx1wnkE80Xyo3JWprkdPT7Qza6Z3/mVOwSSPKwnh/r15hnOd1Oi8VyVagOCCl8XdfQDQPT0FBVlV//9d/gF3/+5wQH5n0SHySPR4yNjTbR7C6XOOSuf4lg+5PFDmpB7lTQisF3pRjY6brGfLHEti22tjYIgxC38ApvNesksV6ia9aIKhA7QcsyqddrvD0/UYiVfJXFcoHnBeTk3LsrzJPSbCn8ERYrHKdCvV7jqH+MaejUKzaL5YpatYKq5XheSqIofPYjN3jrDZnx+M3y+D3rHgE7XO51S6VWNY5AkoiK9pcsy4wms9KtrdWsM5nOybKM4fEERZMxgfj3vo/xtbeQ4os+JsZvv4aUPkvyycvEcYJtmURxTKfdJI5jgiDAssxSOBHg8mjMNyZbPLsjhpyj/aIv/90R7ssDSE4/T4oEeP/+dao/8TTLraRMIOvIsowoilBuTZAnYl5gmibGFY07myu6mVic1kktzyk1rnRNI8tygjCiVnUYjiZnlIlPx9o3QpIkrj62Kwa1ukq1VhWuc55PHcrW03Qh2oAKsIxUnms0iKII19NIZNEyUzWNyPMBIYy4Rh395sd+/8xufziclMz+9XVcL4DhOW7gepdcIpYUjWq1Ioh462um2USND2Et3yKu3zjz+CiMOHZ9JEki9AMMy3xotvb5WEOA/SAsiYHlealqmTjWJMIHRbVaKauqG5WnRAKpf+TBTyxJF4ze1rFOUrfv7JdijcPh5L6vsdttla/Btk2yPH9Xhvk//af/w4lP+vskPkgejxja/E1mUYW5fINmV9ywp2/W+XxJvV7Fts2yN5umGXHiosgqk+mSra0NojghCEJk5aRMbTTqwmMgcLEs84ywHggmryVpGMXN0j8eoyhygTwSgo29Xhe/6N3alonr+qUvxWy+EvOEOAFFxdQTbFsvFWt3t+vErqg2ehvtUkpi/VxxHJPEKmmakuc5/f7wTM+5WhXw5TzPaU0SJl97u0wc50OKM/Lf/T76Vh3larMcGqdpWibWNRM7uXOPddxgswAAIABJREFUSpLyDXmTxzuCiFmvOax2t2i+MWD5Ur9MHOcjjxLc3/0e+s88Te35LSaDIcrjV8rfJ7//NtK/+W7ZVoqANM9xfnqb7t8VonezufDNkGVJeIQXHIc1839eWPSuF9nzu0fLMpl6CzSEsF+e53heQBLHqFqhf1QIOfqywuXimt49EDOxwWyEnAhy5VrV1fN84toKxa2hayrHowmmYVxA66wXtvOL3N7dQy6deu/WP7tyebusSHZ2Nksf8DNzB1lioWzTLJCEa7TU6UW8Pxg9MHHcz0jpfKRpRhiEyJLgyTwoTj/n/dRr16/J8wLqtgUuTJIZLb1ZXpMwSjD0+y+F69dwWsH56mO7YhgfRyXo4X6xe+76vltEofisv5/QVh8kj0cIKU/JrA7zecilnfoZ45b9gz6qqgrtK6eCrMi0mg3m8wWqqjAYLKk6Nt1OizAICcMAkMmslN3dGkmUleQq53Gb+dGq7PHGwy2ajVrpOeF6PrIs02qKZON7Pq1mgyAIcFeu4E9UE8IwItFS1ER4mQdrccKtnGtJjZde0rlxoyix203U0feYmjfQNZfJdI7jVEjTlPlihaFrmIawtc2yjHqteqaEX/+7024SRDHh926d6vM/4HpKoMyDkjfR22gTFy2hb7/S44tf3OS11yQuXekQeHDVB8faIwglJEmi6lQIIt5TsTSXJXLJYGZb8NhJvz77w5sk/+a7cG4ArgCN3z7gTvYt8i8/TmqYoCgkqoKWpUiyTBJGZZtpLQuyhoI26tWSAqIoMqvlCs8IqcdC3jvPcyHjj4SiKIRBiAkcLVysLGW1clksV9wayTzeS7AUk9zJGCVHmFIdGZmMDG3hEJNwlIwRRjIeSDA+vjg4lzOZ6DAu4bZVxz4jx7G70ysX39NoqkajViLOQHA9QCDGrJ1Pos3fZHPzbAUC767RVK9VH0jGy9KMw74gPBq6cJA8POiz+xCy5u8W6+RXtq/0EwMmQ1eZTGakWU4SRoxHoqW79ghZud4Fn/N7h32RRIpqbjKZIUvyGXvZH/X40YMA/L8aEqneRtdUZEUuW1Qgbr4wFL3zddnrup4QryuGfMuVx8HRMYZpoGv6iUnPkVsQD20MQyf2k8KQacHK9alVK4wnc1auL6S1nQqrleCGWKbQuwoCobGlFWiukTcSA/kAOq0Gx6MJYRTjen4pR7GSXydVhihqgGEaqAijJbfl47Z8ckMmioTs+FpOZO/uIfV67aT1EcUMRyfzkzUMUn3I8jvL8lJtNAgjZjORQNtt+Hf/TuLePRiPod3NePrpHMu2yDIxYE/ihCCMHsrR1ZVkRuNJ+f/8neF9E8c65DjH+J2/QPn2AWYUoq1WSJZPaEukckqj18CNxMLR7jRZLl3iOKbbaTGbL2nUxfA6TTPSPMMJDOI4KUUAfT8gCEMmUzEvWqUW0WzOfLFiNp3z9nGOREa33maqDXCzGWZQJ5BjVqaPZ4bl94eJmiLaabPZgslkVrawFFnG0PXSu/585OfY3jvbm+xsb5bw2bh+A3MomPKnSXDvxtJO05TNzTZJdFHvSVZkLEOgxyi0o/4yiUM5tbHY3xcWyvcboLdaDbqdJkoxSzptLuV7F7W/rj62K1waTz2+0awxHk0FPHvp4nkB9+4dXXjsj0p8UHk8QuSSWBi3tjbwvIA0TQmCkE5bDBWvXOpB8TdhENJs1BgcjwnDiPlc7AZVRREeyuqtk+NaOXE7xprb5MXcUEkldF0rGeOapuBULKICDuraM9xwBsXasantMFEOUOINNE2jYlbwPJ8rl7f5Ty/f5DMfebJ8Pq/oqnzyeWFI5BXH8OqfpnsKFPLHr77NtQ0DXdNQFRnDFDd1fzASFVTRzqg5dtmiywofbW3Nnn6P8IOA1XCMqiolB6bX2+KddwQP5Stfga99DUYjmWeeOaZWa+AX7G0vCAiVkIcbYQr4ZF5Ii+Qj9z09gSVJopKBXqsymc5pplVGC5daY5OqDk7b5O5Bn0ajdqYKs0yDxVIQNF3XJy/2aO12nTzLmC+WpUf20cLFUBQc7y7dTgunWuell3T+5ifh618HOGZLviQ8MBS4urnL7cN9PPXhksY6drqbDIcTKrYtyIy9DUZDoeG09tw4H54X4Loelm2KnbUs4/sBjlO5L2/hQcPkPMuRZKmE0jYaNbI0YzIT3JjzvJGV59Omed9jnY8wCJkvVkiyjKYqJZx2PXva3d0qeRhbjSa3v3+PXrfN08l1Xr79Ck+b11FVCVkV7eM0TrAt60xia9ZrFxKdrMhsbnSYTuclTwZZKjcS62sxvn9O/pGID5LHDxi2bQrElWkwGk9FCSzJHBwO6LQaaJpGDjSbdSbTGfV6rRxaRmFEojVIGk+J+UK1znR/gdU1WN5bkikQpRFb2w5Hh1GJBvL8AE3VhCRJ5JDpEbkukxEIH3LahFmEpmm4gYuyinnp9T71iskfv/r2I79GRTF45zjkWpHQbt/ZL1BbMUdFa2E4yrhyeZut3gZH/WPSNBOy8J0q76VPmuegdBw0Qy92wimtZhO+/QqJs8njrYS9OwbXn8zZ2tqgP3AZj6fYBU8mDEMuPf44U955jyeCvF0hrTWYDgZ0O23es6dWhL/wCRZCu8swDZpZThq67B3O2d7eIIlzjgYrwtWMja0Ocgy5m2LYFoQQTiMqFbE4useCD+ROPHq7XV57c0A7j5klOQ3NYLla4Hs+H3nhEl//uoRlwag/ZudSj/UcPnZjduubyHnK0WLOwotJZZeKZxMo91fH3Wn1mA+WLIcu2SqhYVc5eOsIvabQstssj1wqdZt0nhJqMbqqMh3NMSo67VYD1/NpNepkeU6tgAPfu9fHLIQ2A+ni4ir5F69v2z4ZvvueT11zwIdhf1T6ogPYmY4/PKs83B+O6TRb6KZKauQYsUZu5UxnC3q9LuPR9KS1lOUkSUJ/IBj9vc2O6AZYOlef2mXpelSrFTbnHV7P3uITzRdOzttTUFTlvsnxfra4fhC8a9v00u6WALe8j7zJHzY+aFs9QqRJwvdff6ssRYNAtB2iOGY+X7J395AkSTnsD9m7dyhk00cTDF1HUWQ0TUOVhd+ynEViSH2lx2Q6Fw5s8wWOU8Hc1jGqOnt7M4Iwot1o0NtoY939BhvdFtWNCoaq01RbaAsVbe4QhhGe7xOEkeCNGALy+ZmPPMm8YFq/+OxVaraOWjj/PXP1RNfnq5//MDVbLAbr72ka8tXPf5irj+2iqgpXH9vF9wNqVYd2o4Fj2zQKUlmWZTQaDZRiR+bvOsQ/fh30++PWc01G+smnya91iOKELMvY8UPeeGvCN+RNPn2jgaoqVCo2jVqtuLYJhqHT7bbKNp27ZZP/5NPk2gM+yrqC85lt0usiAz557SrLgctitCxFLh8Ua80tx7FJ0pR7+0dMpjOhA6ZKTKZzehsNstQlNzUOXR+zbhIoCbmRkxs5elPDaJpodR1sBamiULtWZajl2C0dahqPP3cJqxWxeXWD3vYWXrDgs5912d5+G0mT0XSV/cM+mq6SKRlaReMN74C5uiKvhciOyuWntnjm6SfJ2/KFr/pmlVm4ZOfaBomRExBhtQ12L21x7E5QG6ogxtUVMdfSVbrbbWr1KrIiC2irLFBI669qzUbVVaFL1XmGyvzPy/+ruopSVy5+OSa2Lb7anSZG08BoGnQf6zCNlmf+b3XtM1+7T25hdcXvbdtEqSuouopTeOqcbjPt7R+VPjBr0cb1eSNLVKsVBoMxV+qX2DF7Z1tYkoAU+/7FRFwamp0Kx6lcYLmf35fYtomiKCwX7gXm+fs5PkgejxCSJFOv1VmuliRRgq4LpFKn1aBer1J1bC7tbnHl8g6apnLl8jZXLm+XgzlZkbEqYnCXpwnddpOjfSHWFkYRnh+UQ/OmXefK5W1azTphFJU7nsmf/e+YuWi/VBybS5e2UVUh8SAQQUJmQ5PETfXdt/bK8zdNlYp90lqIUrF4fvXzH+Z3/+A75c8///ET17lb945JkoSdrU36/SGBqrEsXPKCSAyNozBiuVyRZylVp0IYRFRrNXp//xPYP/cxkUCKr1xTQFMIf/pDmD/9JFWnQtWxuTwa8/tuhYWk8eWP9jgeT6hWKjgVGz8MRFK6slEADcSOTpYl/CAk/dQVav/gx8i14viGKr50heovfgL7I5uYqxXhwCeTJB5/7hK7H3/igizGxcgxd9rU69VCOXlt8CQVlWXOaDwlSVI6NQfN97kzFKrDfsGC77abNGpV0ROvO2xstMkihWutGlox/1hLgfQHI/78Ozf587eO2e8LOZs1r8G2LMIgLLWvnt14gmc3nmC3tsMzncdLzkU9rmCZOpaps21v8HT7qrheOz1czxfAiTDEskxu7x2ws7WJ7/tlIj0PUz04HDBfLIVsTCG2ed974z6SftP7KMw+6PHxOdTS+QU2z/P7zrYc52KrbOMh4MGbm218P2TH2kJG5pZ7t3wezxMw8fPnc35oLv7+IqpKekBVW61VSOLkgVDgOE54z17qD1FIDxL3+iAuxoeuVvLf+F/+O+aLOc88fZ3+YESn3YCMUhl07+6hYJze+j0A2p/6xQvHWS5d2vFN3l51UFWFJElLpMu6XxpGCf3+sfCmNo2yZD44HKAoCs16lf7xuPRxME0DTVPLYajZkgkmGUmS0OpuM52vOBytHvq1bnccehttlNxn5QY4FZODwwG+prNjWxyPJ9SLFkajWWM2XVApdoGD49GZG2r80jvkk5NhalzVCJ67RFPt0ZBGRNaJ3pSUzRmNJiX3ZWd7A1UVLOSxMS2H/UdHx+XfKErhnPj2EGnm0+mIxUPpOBjPiuuafus73H5cLKTXWmIRuPubf4x2iodyOnJNxvviVbZ/5pNYFfOM9Mca6DAcC6BAmmbUa06plDv3T3aokq6TKAqkKRRkwJ16DVUXg9m1/8UT+h5h98e4ffeQWyOxuFzrCGVdTZZptFvM5/PC9ElF1TTkt//jfT9f5XWOE6E+q4gF6fTnxfMC4iQu3RhB8F40TS3fu9lsIRbMPL+wnT7Pr5DTgL2jKb3N7hkgyfnHmKZ5QW4ETuYi63Bdr+RKwf1bRucjSzMWy9V9F/kH/f16IX95+gofrT9P/2j0SBDb6XRBs1k7cz3ud66eF2DbZgnlf1B86Rf+J165uXpfZJAPZh4/QNRrdQ4OBiiqEC/0vABVV5nPl8JjY7PD+NbZx5zuv84XS1p6QqtZFzdJVZCH1jar1WqF4XBEo14Vw8ClS7fbKhOTqQt2d9WxQfLwmwHVpFLCH1duwOx4hqHrbG3ViNKImpmjPa+wkbTxXaG4O55MOPKryHnCR6/VaVQVjPFr+O0fE+S53Ge5FLBRp2ISEaOgk2RpyQeI05zl0mU6X+AHQbkz7g+E34YqK+jPbjEaT+m0m6RJyndue3Db5W++KPGNr2/QbgtE1Ve+kvPWW2KWoioCXnxweIxpGEgSGLUKQ2dMbeWgqiqdRg1JgsGx8LBWntoUhMOCwHU6lI9+iKvffo03Kj0oksflf/AZ3HaT5W+9TFZsomRJIssz5K8+S/hMs1x4jaK/LyuiXWVZJqqq0m41OCxQN4ZpCIFEy8D3Q6Hwm0OLjChNyCVBcFt5HtE8Ll/nad9qy9CBhF5DRSHi0qVtXM8niSNMXQddtGjCIGQFjP/0ty4kkOXSpWJbjMfTU+qwQhts7+4+iqIWbPe85BKtYceaeoKS8/1ALMSnEsdq5ZJnF9ncmWJyzbhLqG2dIRqu9dRycnqbHcL7tH7gonDgtFCMXodhGvQHI3FvnUL3OY5IflkujMSuXN4+A/d9t0QgKzLj0ZR2p1kisLa5v70tnOXKjEdT0d4KwpKbMZnM2N65RBTFRFGMU2sSBSIx53mGbjq0FA1ZkomTEMOskOUScfjwm7ofpnjo5CFJkgJ8CzjI8/yrkiT9KsI8YF78yT/K8/zVBzy2BrwO/Os8z//74mffALaANSznK3meP7qjzf+X8YD9wHonZRo6s/mSJEnwLn0BcyPlKBnRSqo061XiVHge53mOpKhUq5UzCJUrl7eJ44SVG1CvVRmNp8iyjK6phEFYkvZG4xnbWxtMp3OGxwH1qkNIiKaJ+cJ4PEFVFRRF5t7R4oQV3dQ4OByws73JbHGyk84klWZlzuGxQZpe5Ynxn2EBc15gNJ6iqgq37+wDICc+E98vGeVOxcYsdlm6ppXzi2a9ymy+Kq+NLMu8se+y8DM+5u/Dh77A4YHE9eviHHYvZ4DE9tYGnueXZMHeRpvpfMnmhqhOxgQEToyV2gLkkqYkiTDRqteqLJYrhsMJy5rK48apHWixOBkdnXcmC661aiRJwuL5Dnr9s8SDBSvXJW+kaJ0GjScfp3VKHt0wdbI8Y7XyaLVb+J4nZDJGE65c3hZVmR+gqiquJ9pAURyz1ayTpTnLlYeqKKRZRm+zU6KXsiwre+lhELK1cwWQ8TyYLWFXWVL9f9h782DJ8qu+83O3vHlz31++fEtVdVV3V5V6rW51t5CERMsIAhESIGzLA3gmNMahsPDAH8hjTwR2aIjxDDCeGSYQyOARDg8ey0AjJAYhgaaRkISkVi/Vre6urqX7Vb01X+575t3nj9+99+VbatMCkqNPREW993K7mXnv7/zOOd8lneTa+jZLtQobm3WKpbyoIo6oOnq9Aa7jkk4nI22nMJHPZiaT6SxaULvdAfG4jus6WJYwLJtnk88ry+7sNPB8P+KJ9LeGh147bF0dW1mM/DLCJBJWbjeqHuaTzvJSlenU3CcwGLLKi6U8G5s7rAReHJW55wwX+PDxvd6ATDp13VZRODOBAMLLcyx5C0cOzMvlAo1GG0mSoiQyX3HYtsOLV8ecWc3TGTp0GibHF9Jc2JgwniXJFeDitstCXiFnaKztWlzbNVkpx5BliWMVHfd7qBF0O5XHzyMSwHxN+CHf9//wFh77y8AXjvj7T/m+//RtHMPfahxUxjyIoBgH0FiAXDbDNDFDAXRHnNyy7aBpKivLi/jNHTa36lEPHd9nqVZF01RmM5OYrkdy35bt0On2I/0fWZYiDSvDiCMrCq1WB8d1yedSrC5X2NppYehxHMclk04CSfqMWKotYFpORHB8W62OVTiHNxtTTUF7NsPVcmxMixTsIWeSm9T9JVqJLPpkTDqZRI/FkBWJVqfHZDojn88gyzCzLGKaGnEIbMdmPBHIF8/zGEw93u7t4j3yAK3WGrKsYDvCfS+TyGPOZNqdHvFYjGTCEGq3ioLjOLRbXSzbxvN9Yqsu5WQK23ZotHv7mMVxPUY2m2Zo7iXHUCZCOXcflfMvsmFUoJARkveZDKlHK2xu1Tm1VA30jx4Ivl8fyxE8kHa3j4yAkdq2A5KEHouhaarwU3FsshlxaWSyGer1RiRU2Q7wmqHN7u5um1w2RafbJ53JiMWneZXxZMp0tkO+VIWWQiIhFu1EwkDXY7TaPaGpxV7rDObaSxD9H+qogVBtjul6lITwfJAlEoaOHtfxXI2EIarf+ZZSfI7ZnUwmyGT2hBfLxRtAaSUpep6wXXOwldTtDhhPJuRzmajCCL/HMDF43mEOjmXZNJrtKHFcL0bjCebUjLx28gF5zw6uwTDCZLa1vUs6laQ2WeTrnOeN2QeuC0dWFDlqTc237pKpDOOdMYOJQ2tgc3o5zotXx/TGDrYDnaHDetNEUSTOv2bygw/mSRsKhRS8uG4xnnq3xFn6bolbGphLkrQMvAv4d7f7ApIkPQQsAH9+u4/9bo9wSBlGIZehqbZpqm1m+b0dXEMVbZX5k1ZSRG+5WimysrzIykoNWZHZ3KqTTifp9/rENKHhlE4looXo8pWrAj0iySiKQq8vdoC5TAY9FkNSLCRF7Jgb7Q6249Bsd2m0OigoNNQ2siTIYfm4wwZ3Ut8S1c/aoE3rlcv0z8vYf/0ao7/eod9YZWipZK0GjufTcly2JlM2JiZTTewsu90BiqKiKmqk4zQYjtFU0cLLZtK81lG5z+ujnrszsGJVKBazJIw4lQUj8tGoLpTEBZ8aRZ/ZQqXEaDLGsh2WagsUzTxNtU1r0qUWqp+qbXb6TbGL9/x9VcfyUpXd3TadXp+k56EvGGwPxAwmZeiR612IugmHupIsUa/XWbu6SbmUp1jKC/2qRIJMKoHn+YzGE9RACTVkIodSF+FCHwv4McVcjtpiBc9zmc5MFhcrDAeDCK1TKOTodJfothRaLUilmkiy8CsxTQtFlqmUCkzHewPdRqNNMimUkkP2NwgukqoqHFutiYU7WIhD8h3srwKO2pmPx5NIamU+cdxOGNepNPL5DMtL1QggsrPTiIADYWJotQ97gsdiGnpsryKy7cNEQwBZkqNW5DiwpK3vtvZdg/NzrKXaQmStUJss8vX++ehc3vd+jDjNVvfICmo8GnD/qoShzDhRcjFnY04teDx8h8yb7pJJx2bclW9zouTy1tMqs+mQdGyG57rcXfWIyRPU7yEI060e6v8B/DNE43Q+/idJkl6QJOl/lyTp0KcpSZIM/BvgQ9d53t+VJOm8JEm/JF0PovBdFAfBBYfABt+CZn+93qQ/156ZzqZYthNVO74PGxvi4tJUleF4bwCtaSpGMh61P6Rgw3ZstUYy2FFKkkTREbskRZapLaQ4keoTp8rFUBfxky+Q+Z0XGfzul1E/+RLWf3qa7r/5c8pPvcBIX0CVYCWd4FQ5T9Z1OJZLs7a1Sx+JWUxnIss4qTS2YaCpKoWCYByGrbl8WsWXEywvVfcxkGczccCbW3W2tncZu3uJ17Yd2p0e1Uo5uo9t26S8FIav47hu1EeXDMhl0hAQ0tauboLn0+8P8fGxbAc/rqNMp3S3gj6zLLFQKQkkkiwW5K/3z/NU9zme6j7HbnrP06PT6eG6HqPJhG5vgOcJifzheIznQTqAbQ6HY1ExWjbTmYnneTQabaYBUmxxsYIkSfR6Apo9P6C+48SQWKrJril4OamUWLQL+Syu66LHdYxknFhQ8YTff7lcwPf96LPY3KqzVFsQ6r3j2ZEw05tFJp1ka7txyG73euFJapQIwzanJEvs7orN03xyC2MxaK0tLlYEAbXejJBWpeJeOyiM4WAsfOOD93MQUhtek4lEnMl0huu4ZNNiQK0p+2Hj3oHOQZi0lpeqPJJ/kGeH38B0b/9zmzuYQ386agbjujdjRH13xk3bVpIk/SjQ8H3/GUmS3j53078A6ghVnd8G/nvgfzzw8H8CfNr3/Y0jcsNP+b6/JUlSGngC+BngPxzx+v8Y+MfBr7/t+/5v3/RdfYdiHma4UC5GCKuDvdmDUXH2SzX0+0NCk8pwB5PPZZAVhe2dBooic2xVDH2Hgef5aDwhFotx56njAIynU2RJwojrOI4jLnJgc3NAKvDKRpqQK6jk8tXoYlNQSHSeou4vkas8hrt1mVgF6r91HuP5UErBD8Y74v32/2SLmPcyziMrjCciqcXjOpPJlISq4ntu1FPX8ClWF5iMh9Eu76VrQ+7z+nQLZUbbm5yoLWPOTFqdnoBGTkVyKxfzaJrGbqOFueaQy6YZjybks2n0uE42kwqqrRFyCihBe6uHsRTYuCZkOo0+hUIO2xH2vZs7u9FwV1Fk1HtOs/r08xhveTOObfFLv/Qv+dCHfpHa0golq4IpzUipaSaTKb7v4Ougl+LEYjqZrEepXKHTFjtYSZKYBPONQj5LNpsmpmpMzZmQ7q8UxWB7PMENZF76g5GQse8PyWZSzEyL2cxkGdFyClFkAOPRmIWFIv3+EEXVcAJv77BNJdpcJtfWtyOkVLPdFcm5UorQgGHbLERVhcNzEC1Qz/OPFBWUFSVSjz0qhMgjeK7LaDKlnKxEi+P84xYWivvmGWHMq9OGUa2W91wMEdXHvJmULEv7WlaZTIp2qxtJ3lu2HbW9qpUig+GYdEZsXqbm/kRwIzVe2JMxOZu+C93XI5+TWCwWWNbKEZYgdBmdj91G54YyLUfFzWTtv5viVmYebwbeLUnSjwBxICNJ0u/5vv/Twe2mJEm/C/ziEY99E/BWSZL+CZACYpIkjXzf/+e+728B+L4/lCTp/wEe4YjkESSLv7WEcTCWlhYO/e1GieOoyGbTcEC2ILQuXV7O0utYtNUeSlPoDul6jIVKia3tOv3ZjF5DPNhRxInWbvej1pbruoyn4rnCCmSn2aIUnxIfbpF149T1s5Ep1GAw5MHzDcznt66LMPdtH/lPX0atpBgeP+L9pFPCVCewnJ2Mh2ztlLh4EVZW4HSxxc5CGsmy0FD3karmF3Y9rnNtfZtisUAqGcdzPcyZRSxoU4QbkEIhi+YIhdpiKcWQPTayW3HpNHrRYHc+sS4vLrCz06CCmFf95m/+Fj/7s/+IX/u1/5Vf+ZVf4dd//f8kk8lw/PhxnnzySf6X//lf88ef/BTnz5/nwx/+ML/2a/8blUqF9/7Ej4nPJTRJQixcvd4gWqDjuh7tmLOZNL3+gHQqKdBYWSF3YsR1DIINRKCqu7hYiT6fXDbFcDgW9+/0KOZy0fxGfA65aAEMB7fha27vNCLV3OXV2iEobDgAlyWJxWXR+mt/5ePAHrz8epBbEGMT23ZIpZIYhh7JiShmF1c/PA85tlo7BL+9XmKKYOuDMXpcE8ilI/gcIVIqFotFCWIeEaXHdVK+eJ50Jkk+MEdbXqruuQxeJ8JZTZhATiVP3DQRHGyhxeM3trI9KmTpe6dvddMj9X3/X/i+v+z7/nHgfcCTvu//tCRJiwBBu+nHgBePeOxP+b6/Gjz2F4H/4Pv+P5ckSZUkqRQ8XgN+9KjHf7fF9RAb32ysdQX5KlwspjOT6VRhNJkyem1EfzhCjQn0zna9wcpKjUxMmDvZqRQz02QynWI7NvlcFt/zhRWt61JV6ujtF9DbL7ASb9GeJZiVH6MtrWAYOts7DUaqRm2kYH3h4k2pSZIPdCZk0ymsz5GkAAAgAElEQVQqxUL0L2HEyeUzZFJJMukUhWIRSStw5ozPm97skUiAFLScXNfDD7gZrutFSq7HVmvRgnhstUYqKVzmXM/DSMaRZIlOp0cmnRIDy2AXasR1hvoEi/07SjNASV1b30aVFXIBya/eaEX3URSFd7zjHeRyOR577DFc1+Xnf/6/4/u+7/sAePe7381v/tZHOXbsGP/qw/+SZ599ln/6T38OwzD2tTsMIy6IjNMZti0Y8J7nsbBQFOCHoLLQY2KQH5oaGXGd/mBEs92lH8yt6rstOp0eqqIg43Ble0in22djc4dkwsBIxrFtR7R/wir4AOlufsHfbbSiczZMHP3+kOl4huf75HPZiBMzH7J7tMzJwYjr+qGNkzJ8dR/Bz3EcNgJBwmTgTX6zmAY8GUmGWCx2ZOKYf09h4hB/lBgO9lq6hqFHtyeTiT2nxxtIqcN+afQ3Zh/gyniNjiVmMJqqE9PF5sv1RFuuvtvCCcAViqKhqTqFQgFF2S8QGt52VGix60vPfzfGt8Lz+I+SJJURANbzwAcAJEl6GPiA7/v/6AaP1YHPBolDAT4H/M63cCx/IyEh/IsVLYYsyfgIHf6vXvY4vqBzrBJjyU9EVpLhztR0RvuIQ+bMJAacyIte7HwfdGdH8Bo8zxWWlzMTTdMwTYu1q5uUinmyqQTpdIKe69BH4kQ5j2sOqTov42RrdP0izZGKWhY7yn5/SC4rlFPD3ZOdSoHv4/anwnnvZm8+eC9GgNAJwwwGqrmrgqH7eVns+B9athmaCrqu4HvFQ2vdM09u8AwbPPrO41iWjSwTtepAVDNaABhwLIdcNkOj2aFYzNJsdkilkoz1CRr7L87xazP0bIyt7V2xkAfHvbxUZRwMtJ12h91nnqG6soTvudz96ENcqIsEfkevj7q6SiaV5MTxVQqFHGuvrjGqjnjZusB7f/zdvLq2TnVBJMT+YEQ2k2KhUmIysxiNRkJhOfCIcF0XJ7A1jRA6+SzpdFIgfxSFXjCoDaGoALH0jOcu9njHOeG1En7mtWqZ8WTKaGrSbO61zzzPo1DI4TgO/d4wakeFRLjhcCwSnOPgOC7pVALXcUkEooeFQo7im96HTA+PW1vE4oZ+CL0k+3sM6hBNqCiyEFiMx6mUCke2sObjViv5sJXV7Q5QFYV0JonMXjIJCXmdTo/haMKxuQrs2GotMomaj7C6DdFnIJLUnpT7g5iuwtdeHvED9x/jL5/vk02WOHcqxZdfHvDQCfjGhsM9yzJPvmiiyFDMaDR6Nu98MBOtDYoWE49NKZw7meLZV0eA+b1EML+95OH7/ueBzwc/P36d+zwNHEocvu//e+DfBz+PgYdu57W/O8JH0w2ee3VCa2CTTSoU0hqWMyOfUhlMxG4lFRe7mlc2LZJxhWpmP7LFsh18d6/EDRncw+E4Opk3t+r0+iOkJaimxaKytb1LTBP8kM2tOjFN44zeQGleZFZ+jFn5MQDsTm+f10g2m8YLLmJxIY3QEOZGw5lFjFs4ZyVJCOb1Bjiui6qqxGMxcvkM7tPPMyoWYLEML4vdmeknWFkNL0CLY8dyrK3N9+rEom5ZNrGYhuO4bG3tYhg6k+mMcllD8iZsbnWi9kYmlYwQbq7rovQVirEczQDNBpC8I47T9ihks5i2xW6jJY5Vj6EGRL+NVIrVVhtHU9muNylWSzQne5yO6XTKiePLUUWgKAorsyU24ltsWQ1WlmtsbG6zslwjYRgoikK700NRZBzHwbQcUkmBypmXBA+Nv/qDYeStoigyw9GEBYNI2db3/SjpiffqMRrP6PcFMilsybXbMrbtUC6X6PV6wbGbOIE0v23bbGzVqRT3DI3SqQST6QzHcSkUcnhBFRgmGY8bzwHmo9FsR8cShiftLSkJI37kXOHgbACOZmXPt/9Ho/GhCiSE/6pzg3BJlqL7JhJG1KYKj8NxXTQ5OIduIFZ4FEz3kfyD1K02S4bGyZrOp7/e4Z7jCVZKMbbaJv2xg2XBuZMFXNuikvOIKRKlrEYpo+K7LrZjIysKl7ZtBlOHt9+XZr05Y6WkU0p/77Ss4HWG+W2HJMncf0LHc1VkRWE0nnDHvUmQfWa2jMYMWYrjODbtocNdiyrN5n4ph3Q6iTKzItXZVKB3lUwYNJsdJlOh5ZRIxEk7SVzJQ/FlpnGDjYkJE5MzMTFUXOMuEonjFADTcui0O4EHdjqaGYQs6eFoQsKIc2x1mY3NHSqlAnaix1CS4Ahton3hg7aYR4lpOFOX6kIJ5+KruK+uoTx8P8p4RjKmc0ctjWz1j5SgOHGiwLVrPeK6jmIOkBIFNE1lqbYQDH2F8VS5XAJPYjB2UFWFZrNDJp3EtC3aWz2ymTSWaeF6Hn31MDtXLcqMGKM0lH07/nBpOnF8Gcf3UHZ2mc5MmhMLs2eh52L4cZ3Td51kOByLna3ng+8jSRJLkxpbiW1SdoJCIY/rurQ7HaoLZVKJhNAt6/TQFInhcIymKtiOG20IPNcFTUWPxcikk3T7QybTmdiFNzeiXv1wOEaVFaoFcYYoiszQGSInZRazZRqNNsViNiKN1htC0n5rexdZEugxSZKQZIm1q5s02h1BQlteBFkibFRNJjOhJKDI2LaNrhy94x+PJ8iysjfj8T1M0zqUOADc9Mno5+sNpMulvLA4DgzN6o022UwKSRaq1PN8qv5wiCRJ+L6/zy9ElpVoWD3vQ27ZDoWCmA1JknQI3bQPLv9NADyrsSJfaT3FWxffwkLaxfdtzJnNUjFNJe3jujbt1i7JZIKzNWi3OjBTiAGKtkRjewvf98jK8MYV2N4cowDWDDb7/iEE2HdzvJ48biN8YDbdY9a6rk2n1UIPFoeru21OLhQxA0vJR07KuK4V9eDn48J4mTN8lUnhEbHjcz1s244sOnM5gZKxTAs1ZZCNx4WYXvNZfKPEBesUACu6hh7XWdveJG9kGOtx0MFUVHpbdVIJAykQ8gPBku10eoLsF9dpKS7yTS4iH3AfWWa2nEILxAHdp5+HdIr1Shm6Q+4ql3ju+Rh3nU4BFXy3jzB13R+KIjMJBvpeYKkahucL74ReTyQIw9BJpwWyxvf8aGeaTiYgnaSt9nBwiAVpwcJERsELvAlNRSy+nuuzs9MQoIOdXVaWF+kkkxTXN9FKZczdKdlaEkYjpsdWMC5cIn3PabHbH46oVsvs7DSQZYmSUmJXbnB/7g2sX9vk7Jk7efnCZVaWa/T7QyFOqSi4M5NMACQ42B7RNBU9rlON6/u4BuHi73rC2bFo1DBdl1QyQX3W5kRWtHqymRSypNDr99F1jerSKbIpBTPQ6BpMXNKxGS9vSWSzK6wuJGh3fLQ4zGyfuCbhIaGRQNI9dFVG02QaPZulYoyd7S3xGsG56Ll+JCsfhnSdwa6r55HdGZ5y49bX8lIVx/WQIdJvg70huuMIm+brzTuuF2HrL5/LRGz5bneALEs31JS6nXgk/yBf3PnSPin32XQYVdHJYAZmGHFSqUT03mbTYURWvF78FzUwfz1uHJbtsHZ1k7X1bU5eB40RLhzmzBT3vbrJmeQmV6zjbATDclmRUVQFQ49TrRTRFIlqpSjMZdo9GmqbePOrbHOcV6wcpwoZjNk0aoXkjQz94UgkmJFgn3uqRyqVYDia7EtMpmmRy2doNjsYD58i8ZPn8G/ATpLP5rj4pnspl8v06yOWG02Uh+/narmMKkucKhUZmyoPPeRz/lmZ88/KyBJsrB8mebmuR2bnSwBIU6FRdG19m2w6xcpSFdt1WV6q4jou3e4A3/PpdQfMpibD4Vi0KoKWQkkps6St8Lnff5LeawO0lsZn/++/4NUvX6Umr1Cr1Pizz3yORruDYcT5+O8/wWuvrfHEH32KC69cxDj3IFdeeZl771mhs77B4mKNS5df5ZKi8Gef+RxPPPFH5AtFmq0u559/gfPPv8Bzn3uOqTfjL7/wJapLVf7sM5/j2rVrSLIwswpnALlcJtDJ8ikUC9i2gxUMu3NpDaQJ9d0W/TmZmKV0hsVUmuVMllQqw/Hj8PyzCulMjtMLJ4RApiVIn77vo2kKqhbj8y/2sCwTXZPRNZlCCnZ32zxwMsmrdZuvXBhyaXfEaNDlmStjOiOPr1wY8vVLQy5tzUjEFb704oCruzM21q/hue4+cIh7BNP7hl7b0tEy/PPhOMLXRp5D2c3zQL5VpnUymcC0LDzXQ9OUIxNHOOD+ZiKcgexXCZ7TKAsSxlHtr/9S4vXK4zZCQmK9o3K86PG1Vx0ePakyVpYYmC61YoyvXrb4/nszrDdMru6avO1escv4i+d6PHAyRW+iYhQE6euvmjWq+Ri6KjEcikU2nU4eYq0DnIpdZat+N43qPZTdFNb2LqOxQPW0WqJNJaC6spifJAxiEpSrJdZ6JqRSAhksCyG4MJmVywWuNLucetd9tNodEn+5huSLnnvYKpDedJzlf1BDvtyHZ3dZkxe44+H7udIZcHfS5rWRT7PZwPd8vvi8yWpuRCKZZnvHpbZY4dq1BseOifaF6wotp8HiW5Cu1HFcl2KxQLPZQo3FsF0Px7a5tr4tmNiuy2g8IZlKRDIgYXQ6PS5dfpVisUipVKJarTBLzFhaWuL8+fO89NJLPP744zxw/30Ugp7/0tISDz10jmeeeZaHHjrH0y+9xLve9SP8zu/8OyaTCZPJhHvueQMXL16i2+3ywz/0Q3TaLXZ26lQqFS5evMjS0hLP/v6z/MIv/AJPPfUUp0+fJp/L8NnP/jmmafLWt76Fly9c5uyZO4UHhiQx6A+iwbYsSSDFcV0Lx7RQVY3N9W1OHhgD2N0eNjBU+mxuOiiBIoEeU8nlAoOmoC30Q/fD9vZ2JAwIMJlO2dpY59ETSUCQC21b4WS2T1KBu4ojiqUKo2Gf2RTuLg3IZFKMRmo0SwihsCBmDqH+1Q3D99lu9ALOhbOPowE+x1aXAKHOYM0ZSIXH3Wx1kRHziKNan7cTYctKUY5e5kIFgFDvTRy+T7c7YDabRdfJUYN12EsgEvDG/IPMTw47XcE3up4E/XyYMxNJliOo+/dKvJ48bjNaA5uMYfHWN5RwPZ/BZIDleMRUibfdm0aSJGzX5233pvE8h42Wy1IxRj6lkE8pXNqcstW2uGvJ4FhFx7LBd0VpHnoiO66L5/ncbawzIAflxygCQTcGVVFJxGP0ZSHBkE2nSAY8j0Q8RiIew8cDxMW5ktCFh7keZzSZos4M4qrGxmCMMp1S33WJ/50zDMsJ5J4pkEPTKbljFTSzzeyySfnh+/n6hQZMPRqjKSupBK/V93Qsq/4aUGN9mOJ00GkYj0T7brc+xfNcZqaFpqkosozrudGgU9h8WvR6vQiGOx3beL6NJEu0212y6bRoVwUXY6GQ444Tx7FxOHv6bgaBPufZx+/m7ON3R8dlrjt0uz1Mc8bbvv/76HR6nDx5B8aFi6jJBJeaLd72Yz/GqUKGly9cJpWMUygUeNOb3kRpY5POsRWyWcGUf+jcfezsNHjssUe4fPkSqytLjK0pl/xXOf2WN7BqVCM2vYDddjl75k6hhuzFIFCyRZbY2eqSzWeFMGLAkt6dTCKF3Ze29hbW/lgibTiRfLvv+6RTSeq7LabTGbIsc2y1FpHrbNtheanKcDii3mgT13UWAsl+RVWQFZnReMJ4cg3f9wUSbTJlPJkiSZKwBJ4Db/j+0b4ZR0W89TWq1cei30OOxVFx1MyhPGfqNB5PaLa6+/72zcQ8emtnpyFscD0PWZKi2UsYiiwHrSWRQJsBAfF6ESaQr3efQ5dj3Bd7Q3DsU1zXo3QjDbC5aLe7pJK316L7247X/TxuIx48lfaf/Pi/AjjypN7a2qVaLUftoYOhx1OYgUSz1n8FO3t63+1rVzfRNCGXfYe+Qyd+d7QYhbujmWTSXG/j+UQ+FtVKkUarQyIeJ5/P0u70SCUSbA0GLKSSWKaFogh7zV1bZKC4IqPOZiQTBloizac/9UkMw+Ctb30LAG6jSd5IMEgnyQeLp6xqvHJ1l9PHF7DNCZ7vsblVp5jLocSLTCawuS6zvOqRkPtcfWmdTDLNxBeLo5GMk8ynaPfaPPf5LWRZ5u+89156vR6aqpFMhUSvVrRbTSQMjDnYZLvdIRYMPdOBEGGvJ2RDDt4XYL1nMJq5rJZ1FgsxVFmc77Y1IW6keXFri+3dBO88t/dder6PY1t0+irVssKrV65E5NBQ76jV7mGaQghyZlo0s4LlV1JKlKVC9L29fOEymiIRiyeI6zrxeAxZlg/5fevNr3HNO0l1zqxrc9Dn4i4kYw5vvsfHZQUQiWm+yhCfWSdQIdCiwbZtO9SqZWRFYTKZHtm6GY8ntNq9SDbd9zzx/gM4r6ZqDEajQ8Pxg4S/MGKtp7FKD0e/H4WSmo/r7erhcOI5CAu+1bi2vk06laBQyEU+6gCD4RRNlUXVHRd8ompegF067TZxI8HUdLBsm3KxgKodrgw8SeWF4SVmnomMzMP5+w/d52YKFPNxu34eH/nAk/cA/wPwrz/40cf/Rrlyr1cetxE+e0xe0zysebO0tLBPKvpghIkDQLF6hzy+ZVkmm1AoOBusWTWwhuzaLglVETLq3QGjyYSVlVqks+T7Pq12D0mSBLlwMhXtK1VDsx1cxyWTTaNpKvXdFrplISsKiqJgmhaO4yDNTP7uT76X19au0m53KBYLFE6d5A/+8Ana7TY/93M/x2DQI5PJcfr4ArvtAYWUynQsrGCNZJwv/JXM/Q96TCawUJZ44mMvYNs20AwWsxDRJa6LXlMMzf/iD4WDoRLg3yVZEscUSD+84ydEcsnlcoxHom0VXsTh76FvumEYOLYVQXIBCmmVc6dS9EcuX7s4JBVXKKRV8skET73Y581nqoxHY/7qxT6P3p3m/GsjNEVmNHN59O404/GYZNIQkiCZNK7jYtkOtm2jKgqW7QhOSH6ZZrPDGtdo0YIunFHvEu0rRBIJyYPXi31CfMk4KdcGJqhKDNiTITmKmV0uFyiXC5Hkx7X1bTRZptPtI0kSyWSCXm9ANpPe14cfDEYkApFFJ3hvYRUzHE0oFXMkD0BrQ0hxMpk4bArl72dZ+x6Rz8x8Gyz6fvLZQ+8lTBoHZy3Xm7Nsbe+iqeo+1NV8hJL5IODKGxs7xOM6K6vH+OVf/mVOnDjBj//4j/HSSy/z8oUBhUKBc+fO8bGPfYzv//634roacjx9SNgvjPuyZ7kwvMzQGfF09/lDCcQwdHZ329iOzfJSdZ9KwDcbH/nAkxJC/ulXEcof7/nIB578Z8BvfvCjj/+NVASvD8xvM8IL5Xpf/sryYsQPCCPUlZrvfzpGDcmb7LtfpVhAd9p0E2dJJRIkjDjlRIxJYFWay2dwXY+NzR1c16NcLgRaUza+75Mw4hhxPRg4izkICHRPsyl27CsrtWiupygys5iOE7RNwvt/8Ytf4g/+8AkmgRqp07/GH/zBEzi2xUvb22CN+ItntjGScbLpFHo8xdt/wCeRgLvO9hmO/WgYGVYQqqKgBr3n+d2jqqoRcsdxRbKTkFAU+ZAEPuyJ2RmGQTKVJJlKUiwWoipkPnEAlFMu/X4X3AH3r3iczUzY7VkYMYczK9AZjUh4O7z5TIL61jr3LCvUkn3uX/EYTMR76A9G9AcjhmMhiJhOJ1lZXiSuxwAvWow1TaU2WaQyC9B33nr0nZ89cycnji/z8oXLXL5y9ZDN6hfV4LjTBo6hMZ2akZLy6dUMtrvEsdXaDbWmNrfqJAwB916qVVherVEqFQSkeTAMWO3DfUkqm81E0PBSqUC1UoySlOd5GPH4oeF1uVw4NI8YBYncKjxAvPnVaOKtauoNE+ZR6rSjQPTz4OvGYkfLfSzVFq6bOEKBRsdxqe+2KOSyrKwsRrDoZrPJ33v343zmM5/lgQfu4wtf+AL33nsvn/70p/n7f//vcfXqtRsSGsM4k76Tc+l78fD2e6J/B+IjH3iyCHwG+BUggVjHE8Hvf/aRDzx5cx/eb0O83ra6jXjgVNr/y6Bt9e2IePOrEbFvY3OHO/VrXLGOU8zlGAQX48KCENfbrjcDQb0+IJPLppGDnSVAsVig3e6gxzSmlk02KNPNmUmn20dVVZREgmFwH9Oy8YByoCO1uVWnqqjQ75O8/35e2thgJZ1gMByTd9dRq8Lj4kqzi9/vk8hWeGVjQCpmU82IRa4+cMkYkI6LysB1PYrFAo5lkc7GmE6JKpsn/0hU2I++8wTJVBLHshhNJiiyjBH4SOQKKqORHD0G2PfzwXBsi+l0hmU7h+7TbLY4cXwZ9+nn2a5WMOfaQ0t6DCMZZ2NzB/9yC6kr+DC67aDkDaZvqJHJpJhOTdLpZNRquba+TbVS3De8no+nus9hyHGW3MVDtzuOw6XLawCsrq7wsnWBhaFoDaUSRrRD//+erfMD91fY2KoT09R9LZ6jXAQbjTa+D8ViNtL0UoBsPoth6JE8SLmYZzSaMJpM8TyPYys1bNum3tgjXJaKuai60FQN27HJZTPIihxpPx0M23YYDkdUWcdJn6Df3CJdOy3QZtYedPuoltf8c4xGEwbD0S0t3DeKsBJzHAdZEsTYkCASN9IwXMdLLuHYJrbjYRgGo9GYTLAZuXLl8i1XCSHRsmN2uTK5ug/KG7LqLcum2RKV21E8mZu1rYI21ecRWoFH9cJMYAS8/Tvdxnq9bfUdirA0tYYtUrYYZPpKIPDnWsiu2NVr/VcYyyUqpQK7Zopyes9iVpbkfQM913UBmezuDG1NtMDKBF7dq3GmkzjlcoHtwZhCRgxUFVlGlhViMY1Os0U+m8HSNHTPY9rpEtcFiqms2Eibu0zP3I0/7IGioMd1SrEYRvsS6402A1VjIR4jXT4OQCmt8KWXuzTGMicXYgwdD03xkCSXVCGLNZoyHo2xHZtmuxMxi9vtjtjZ+j4z0ySZStIfivcTQlrrVgPzss2j991HsVhgOBhE1QVwqD0FoZ9GDse2orlJmERkWeba1U2WEdXhlY6AyJ7KpVlb3+JEcpn8i21G//kZkMAGbEkC10N69z2oP/FGJNmhvtticbFCo9GmtlhBkWX8oIKcTyoACywwyPa5Iq1R2alwfHEpOlZVVaOWFkBynGHkjlkuVIPvWSQOEDDu1eXFfe2mUMQw+j1oCUmyjDWZRolDj2lUq+VAnXmCbTucOL4ckVFri5V9XurCzEsYdPX6QzzXR5LkQ0PvTrfHbHaYQa5pKqlUghFncGwXN1Ej3vwqMUlFm5uF3Cg0TSWfz9wYDnwLMS/AqarqPhXfePOrzEqPgpqHgJfVbAqb25i6x+cKE8e84OLNoqDneUTPB8ZiIoGESTAW06iUizSa7Rs9xY3ijSD0NK9zu46A1jzMd1gv8PXk8Z0Iz+NU7Co0r0L5MSxKR95NMdu4epFwCZxOTeqNNrIsYVk2iqpGO77IWe8rV5l95hIzSQJJtMIkwPjJc7gP1zAth1p4ofs+RiKB77m4QRupO5mCOyKbSlANLiRVdlA3OwxPnsCeTBiNPJCVaIG9B2i1O6yurjAZj/cc6QCQSSQMFmuL7Ax3mLk2cdVBccE0zUjbKJVMkMhnGNkdnC4gSWiqEi3uwro2w2giFrjj6RUSVSWqrMJqJBuYEh1MHMPBgMXFGo3dOqomnPFK5TLr6+tilpRJkb6yxs7iAkq/CTMffzLh2kjAaMd//jKj//w02Iex/96fvMg4lUR58x2A6LE7jktFU8WMqyYWmLAqmPfvrtlViiWxkDS6YkHPKhnuSt2BJEvRovSGykme6j5HMmlw6fIa6bR4n+84J557c7u+b5Z2sOJQVIVeb4CmKmjBZ+Q6Lqq2J+dimmLKJgiPwqa4FeyCVVUwyCVZtAtdz8PQdfpDsYiG5LZ5ufTQIGp3t43ve5FKbdhe2qk38Dyf7OqjSBJHkgd7vQGj8eTI3b3juFiWFc2/QgJdyJ6PIkCxjcdCJDRsFRpzysYgCIiyOyPWOR9V/GEMBiOqC6V9A3XYG/jfauKYj4cz9wtF3sRxCnNKw6GqQlipzMeNkF1B/Anw0ZvcRwH+39s+4NuM12ce30K0W93IrwHEbibe/CrAPq2p64WrF/fNPVLpBNVKMfK6dmybhYUiy0tVSsU8pVd6qJ+5KBY4ywHTQbJdsF2mTzyL9tQG49GITifQl7JsppMJqbBFIMtg27iGgaZp9LoiOcQuXMS55zSptNA9sm2bpYRBfDqhmkrgEmPpxArr6xsR9FAOLt5TJY+ELnP1qkS9PWa3Z1FVVMzpdE9uPWhF+VMLdaKTSAhlWtf1GA4GOJaA8PaHI1zXI74cx/Ncer0pg+EOlu0wnc0YDgbMTCE+OBwMmE6n0T/LdnAcC1lWeOKJP+LZ554jmUxSq1X58pe/jOu4uKrgSoxdnVO1SmDvGkd9rcPw408dmTgAJNtj+PGnGH9jHdd1OX5cSHBsbtWprdxBPHmYNVyvNzl16q6o/fRI/kFWZkssDBfQx/HIcGpNvUZ/tqdi2xx3uf/++0ln8vzgG1ejv+ey12cmX1vfZjSekEmnAqHGNLbjYrsusixH/BhZkVFVBVlWWFgoBvMmicXFyr4WiiwLFN/UNFlequ5b2EMV5GqlGHGSCoWs8LH3/X3ug8dWl6Kdvuf5xDrnxfUymjCdmozHE1KpBLkABRaq6UafuySxUxfij6qqIisysiIfJt5JErIiqqNCIUcxcH0slvLR3EzrX9p3bR6MyXRGfbf1bbWBlRWZR/IPsmu1eLr7/JG3H3zBm6k9fPCjj7eAF27y0i8E9/uOxuuVx7cQiqpEmklu4wVmlRsni6NCb79AP/9GdFVha7vBsdUahXxW9HxHe6Q4+5U6w48/hWRfp5S3XKw/Oo+dUtFOL2DOTFaWqmxs1YWkue+iAMgyJ8r5yOM6/eoa6rk76bTHTKYzVgLMv+O5ZDNp2vUGJcNCkVVOnjzBK5de5f3Lv54AACAASURBVOyZOwVfAYGWyWbS/MUzl6ND+cK2w9tqYGd0hlKXtFvCdl0812NmmsQ0VVzUvo+qaUxnM3K5HNPpFE1RmGKhrCjM1i0y6UVkWcHzXNKZDHVl3o1uuvdj4JK6sFDkzJkzPPnkkyQCU6z3v//9zF5+GR54Q3T3K50BimWTzehY3Sk3k4b0fImsFyd98pRQ4r37btZee5VkXOXFa2PCfVgqrnB8Ic5IltlomVTn1nzLdqhWitQbbU6ox8Ru1vP5xvCViOvRokXNO85o6gIqemAp7Louw+EYz/NQVC3SQ9vcqkcmWeLrlVjf3Imqn82tOrlcDlwX3/OQJIlKsItOGgZGQtjwFvI5qgslTNPCspwbymjMI4d8z2e30RJufZJEtzfYl2xCmQ6JPevbsN/S6fQw4nGhkDyeMDsAaRWmaN/azAP2Zoubu20yloRxWJdxn6LxfpHGW2eIX08r60z6TkbOeF8bKwzfj0Yw4vebacyJ+F3gLGJIfjDGwe3f8Xi98vgWIjxZFLOLoYov3bGcGz3kULjE0FUF09mzYu32h0xms2jX5ns+bmu0/yy7zvHkPZVCPiukMHZ2yaZT4mKYCgE8RZHF73qMYwWxK/blBPG4TjqVYLveQJL35CVyc9wAPaZGWk7VSpFKsYAkK/zlF5L8yPedQh3dSdq9k7O1O/nypRO4ionajSNLcoSOCpFRvu+hqAqe62HZDsPBgMlkSre/J9cRr8RxPY9kKkns6pNHvmfZkjmTOcOJ2Als26HXG/DQQ+f40Id+kXvOnuGRh84xHHRx28Lju77bYiVAlZ04vowsy8Kh7yZrhCyBabt87eIAy5W4ujtDSi5hWSaG5nNmOY5l+6yUNJ69MuLMcozO0GE63u+NMRiOWVmqip168MTlWTlaVO4rirlANqWQTYnvIeRwpNNJLMsmaehsbO7QbHVxXY/+YIQqK/T6A2R57zu+tr5NuZjHsYWnSKVSZHlxgUEwX5IVSUjA+D7Nlvh8ur0BpmVF1rFHfhaKHJ0XkiwcICVZJINyUJn2egM6nR6NANbb7w+xcnvJ25yZQmY/aJtpmsZktv+zcm+BnR2G4zjRe54/9ruMDZE4tuosLBRviW8xjwC73iUXepTMR6fTi2DMByOlJnmkcPchJJYkS/taa7dY+XwC0Zo6KtTg9u94vF553EZInh2VviDA1TQFPNEqCGx3aE17y88ZwFd1de9cME2LuL7/JHds++ZnliTR6w9Yies0211URWE4HtPtDygV88LFLp1iELQx3Fcu0V5dRu30mM5MZElCj8WYTiaYloUxdwzteoN64KcdW1jg1bX1aOB7/4MeV6/KGAacvb9L2shx6ZLElrMLWWjSPKSRqKoqsutS9H36CQPdMHAsC9t1cdhrH+m6znAwIAa0Bm1UW2WxUmLLaoCbIq+puK4gj/3qr/4G73vf+zhz5gwAH/vYx2i3xUJi2zbZbBZVVfmv/+F/BYgdtK5rgul+k8/W93267Qa5EzpXX9v7+1og33VlCGdXa6xfu8rZpQpXrrxGBjAMMacI4a/lcoFr65voehzbtrFtN6oaHsk/yOeeehWA+1ZiTKazCGHluR6m5US99xAS7mgqiYRBp9+nkM2STBi0Oj2swJiqG8DGXc/DnImZ2vxuPlT1FQNzBUmS9u3Cj4pyKb9vwdM0Ic7pOE7UylJkhVwuEw3UR+MJ2ewCsc4LWIX7aDQ7VKtlUkkj2umHLaZw+H9wZ3sUWz3051BVNeKshFLr2ngDJ3UCuD60/puNo7hcBzksR8Ujhbt5qvMcOS3DXSnR/my1OmSzGRKJ+E3bVgAf/OjjOx/5wJN/CLz9iJs//8GPPl4/4u/f9ng9edxG+LJ20znGjeJKZ8CpwhxiyPXQlL3Br67HMOI6na6QNJ9n1Dqud9PKw/d8isHOr1YV8M6VpSqyIu/Ti8rFhQeH8vD9VNgTJtRjMUaTCeVygdF4RiIeE4q/HbFTz7oOrucxjekkk0levnCZU3c9wKVXZBIJeOCcx/ln8zz6mE+xCMnhAnFN3ccrUFUVQzF5zbsKEuzYFglV9BH6w5EwgRoqmG0bGQkjZWBe/iwA6atfpF59CM/1uSOzwmuDDfxYIO2yvcOHP/xhPvGJT/CVr3wFgHw+z/vf/34+8YlPsL29Ta1WYzKZRECARCIueCOF68NGw5AkKJ6s0US0U5aXqtHAM1z8QoTd1vYuiiKzUClhWhb1RpulWoWtbXFNV4oleoMBhh7HtsdYloVtO7SDWdVb7inRbLYYj8ccCxK0rIh51fwlm82mI9Z4NpsGz2dnt0lM00gnZBLJNIrigmuws9tkOjMjwAEIlnxtsUKz2WFlqRoNokNv8rB1ddSiLUl7zOlw6KuqaoQ4C20AwliqLUQbr3jzq5xMJpB714jq2iGc1IDmGkuA10mwqGrQq+PLGsgqBRkYd/BQkHHB39tkDEZTFtJ7743xBpujxA05JgeH40dFCCn+Ztnt+8IXz/XG7AN8vX+eC8PLnEnfGQEt5sEIN4sPfvTxn775vb6z8Xry+DbFQabtwejPZpzM7ZeHUA8gLaoLJcyZSSqZIJ1O4jhCz6iQzyIXkqJnfYNjkICR5pMAdupNVpaqDAZ7rHZNUxlZDluDDVonPR6Ze2wun4mcCkN4cLstFn1ZLlOtFPBcn/5oxIl8mjWA8ZiYMqZWqzMzEly65iClHPx+mmTNwfVTjAF0SNs2M9MU7QU5wY/+xBvpdrvsJOskrRSptlicprMZsqxgOzbpYg5HmeC+9QdpfaNDJpNm0YhjJOO8fEHMWOrMMHMiqdV3tjh16hS5XBZFlnAkhWa/Tz6f5/z58/zsf/vfAHCp3eNYWjCuYzEN7iyTft8jDP7T146eKakSxnvP4d5R4Fg6ycbmDp1Oj1w2Q/srH8e54weRh2PKxXwkhTEcTdgNbG9jmkqr1QXkyDscH9q9PdXh7Z1GAGV22GqbnFhcoFzM7/Me1+N65I53VNihSVdcZ2t3wOJiERyP4WRCoVhg0B/gef6eo2Eui6apUTWzs9Mgm83QH4z2tSv39eGDJr3vw2Q8idpAoeeHdASxE4T8yqv2CZZqwkd+MXC5vJ7MCdz4mnJhH1rJSHFIsSFmDQ49bj5ux8/j28mHC50Jr4yu7mOkfzvmO3+T8frM49sUpmlFKKeD4bkezYkVLQLDoZDuno5nYE3o94cMh2M2t+o0213icZ3hcEynI9pM6XQS6a4y2nvug9jRrU5fk7F/+C7cE8EcA592p4dh6OTymcDwx2E0GtFyW5xL38vGxva+3U4ohb6ymCFfMFhZqlKtFFlZXkQarmEk41FLIzaZYJRKrK1vgqywml3kjuUSj9xzB3qpTMKy0EYjShJU9Ri2IyThSwWxUPV6PWJajMXRAsdSKxSLBaHV5Xl4RZ+nnvo6l1+6SFopsv61TT71qU8xGAxJplJcvnKV2uIC99zzBgzDwLIdNFWj2xtw+uwb6PX6ZHMF/q9/+29xrDHn9Dj/8Gd+hrVrm9SDlp1ABdnRopB851ky/+BR8fke+Jd8yzLThxbpD4ZB+8mnUMjRfer3AdC0ve8knUqQTqc4tlpjoVJieakqtJ0kiZimkstkUBSFbC5LKpkgpmmUi3mqlSKKLHEifycnVyr0J0ks2+HCxSt4rhdxFm7kSSFJYrDe6faFwda1Or4UR5IVppMJRiKB7djocV3MmuZIe57rsbhYiRLe/OtICAHBza06m9u7rF3dZHOrjhHwdjzXI5lMRJpuwohJRupdId78qkgc1rFIYWA+wej6YdZ4qNAQJo55wcf5cNwbzxdnB2YoB+P2EsK3l0x9rbuF4RokzSQX6leif84R8vffrfF65fFtiJA9GqriHgxZkcHdOylCUTxNU8FJRBdqOp1ka3uXZMIQ0MM58bzReELmHaeRVRXzief2IzR8H+U99+K+cVkMyrd2I/kSECKOYc93PbbBuey9uJYb+Ykriky93ozsT/PZNJIfE+5ywfBw6ij4M5NGq8PS4oIYYagq0/GUeC5GOinxx3+cZGUFHnpIPHFc15nOZhgQCRf29BFKVsJ9zcO2BVrKsSzUWAzX9cimU6hmjL/7k++lPxjwG7/xER544AF+7uc+yO/93n/kk5/8JO9617soFEucP/88f/qnf8o73/lO/vqv/5of/uEfpr69yerqMr/+67/Oe97zHsadIRnHwccnpqkMLLE/DWXHYU9TynjHabxigslOB0VRcGyX/MkqowWD8tV1pAfvxbZtLNuh3x8yWvr+CD0VRiadxLSEkVH4/cWDVmQ6laDRDnShEkJuPptJMxiOowH6pUvQdTbRrVUWKgK6urFVv+VdabVaZjNYbGVZxrIsxqMxCwtFdnYa0UBbVmRmc8lDVmSazY4wgTqiegjPn253QD6fiYyPwseCIMApisJx+QqWU2Wsr+DnhGnZMuwNsn0/IuxtbTeoBW2bsC2UzaajhKEoMitLVdqtLplsWhhuBaTF68mVhDFvM3sjAcZbiVuRVr/VeObqC7j+0UniFtFW3xXxujzJbcQ3K09ypTNgJZOkvr17aBGYV9e9tr5NTFMplYQ9q/Abn+B5noDEZtNsbtUpdRzMnT6aruHYNmNdwrhnmfF0SkzTcByHhUqJaxvbEfomXJgv8SqP5B+M2icxTUNTVZKpBLuNllgkpAmSC6+tCxMlRZapZnzaE41cPoPnCsjnq70hKdsinjgWvZ/pWGZjAxYWLgqxwnicadpCiUu4Mx8lLrF2fsJSXqNc1mi1HGKxGFowNDYtm1ptke3tHVaPHWM8HojEYySwbIdJsNAPTJOMrpM14iiKynA2Q1N0bNdkMBljxHSyusqldo8Tr63xlAHnTp5mY2KykknSabZZXKywdnWThGGwsFCkXm+STaepNwW3oLpQ2vNMD2ZEIHbWrutxbLXGaDzDMmcMR5N98FghiS4Ilbu7bXx8Yd3rOPvY38lkIhCQhGyuQDKQ5XZdmytXXsOyLGKx2Dfd0nAcB9t0MJJihjaf1Hq9wb4kCker4M7/LWwlhe05SZKj2YjszlC7L2KVHt43PD8Y84rU88ewsbHDysreILrXG9CfG4K7rnukpMf1IhRkvF7cyswjjNtRxr1Z9Kwxl7YuAXDu5Dmm5pTd3i79cZ9f+NDv8+La7HvCQer1yuPbHPsx4nuhqwpeOs1ad0gtaTC2HfIHTsaDC0ShkItK95DQt7xUhSXw7yhhJONsbQneg+d7lAo56o02MU1lPJkiy3IwqG1QLBa45lylJJXoBGgcgEwwZDQtJ1L8DEl9qiJjmhaqotDuWxiZSqQRtDUcc6qQYTgcs7F5nmQySWOsMph63FF2ABlV01BjMaxtMXcJq4o3nEgyGg7x7CRJQ1Qd08CadmaafOPFlygWCkKEMYzRfhHJO7IpPN9jbe0q1UqRbrcv+AKTKaY5w9PjtKYzFE1jlDB4ZDKFF1+BY6tsDMbEPY/6rtC7CoUrTcumNxiQz2Xo9gYMBiMKuSy269JeWaL89PM495ymmMsJv3JPSLebphVVb7bjIMsyjcCXO4xwnhWq1g6HY/5/9t48WJLDrvP85J1VlXUfr97dlyS3JEuyLCSBxwMxlsWN5T3GLMNeLBAw/2zEDswO/2ywMzsTyx8b65ndiN0BExxrDIaBNd7hsAmMMcMiC1myZal1dav73fXqPrKq8s79IyvzVb2ru2UWW4S+EQr1e6+OrKzM3/37fvOFIpIk0+t1GZtjPGfKwJny3Bt98opHPiOwtlwnldHZ2t5HFIVTp3zOMtTTsUUqozMcmhQmX0NRC4z8JbrdPhm3QUkYM22myYrjhA1XBwInjVN8LwgCeutZXHmV7l4DRQwQBYXuXoeSJpHWdIbjCaI3c/yhxih1PxlYOJ64b7O71yCladi2TRiEhGG44LyCMFzgzCoUciec293gdmWt8+LmZrNDSteTQYFUSrsjNtzznCZEJbk3uteRBAk/9DE0A0Mz2O3scv/G/ck+zDsB7/Y87gLzafBp2Ns/POE4muYUSRC43h2ykklxsZhFU2VKGf2uJCptx03q3jdv7TIwI4O8XK+CAJl0Ck3XMNJRDyCe5Y8XDwHGwRSpL+G6PswIpodDE9McMxhENCCry0s4bqRhgCAQBEGiZdEbjNjbb7LfaBIOh9zsjRhPIqOvaSrmxEEMParVxVFPVZEpl0vIszJDr9sln83S7HSZzurSrVabyWSKaxioS0uMTtFOWE9rFAOfigjNdpfDZiciMDyIaDBMcxzpUQTRTawoMsVMilE+R2M5uumvVIvg+1ipNPWlCq1Wl3anjzmOmGUNI4NlO0c05KKQCFiFuoZ0cBg57f1DEKPzI8w2sv3ZEl4pn6dYzNPqRDK7S0tlWq0umq7RHwzp94f8xqd+i9bhIZ/4xCf49Kd/m7X1df79H34O1w/58BNXkJlw7do1nvvKC5hji1u3bvH5z/8Jiprm//y3v4wiawwP3kCy2mheH8lqI1ltxFET2dxGNrcpTL6G1voyjuNiV5/Azd9HStMRRZGGneMN+R602lWamffSzD6KVX2SUeExnOJ7UXtfT/ifDicKklGj62aYhmlKq5fJLl1AypRwQo1AzuCL0QDCac3v+Dr3/YBypTjTL1+Uuo2XJIcj8xvXoAVMc3LbLEV+60/O/JuiKCcmzO5k3Pc8xwFRSe7xi+/DD33uXb2XxqBBY9BgubSM7dmnlgy/VfFu5nEXOHcGOwiSizUeBS1pKjUjMkK7AxPtLndA5hESUi0X2ds/JJ8zcFwv2R0QRXFhVNTcnlLK5xmNx9RqZRqHbaqVAjjMKc7NasndPpOpha5pc/P2AtNZNB6T5i0XU0ikyKRTGJk0ghg5xDAIyOWykdEWZmPFjkO5XEqyiXmn2+/3qZRK2E60y9LpdpFlGXVp6cS0DMC6kaZj2SwbafYODpPdk9jwT8YTtqJOcaK0GNfOq+UivcEouen9gwZb2/tcmn3u690h1WyalA+maSLMnOVStcz27gGpmbb25sYKRjqN/OB78J//GlzcoFIpJ9ottmXj+iH9fh9NVen0+8l7xiOe5VKBwWBELmvMpE57TGeaME899RS/9mu/xo/92I/hODYf//jH6fV6fOQjH+Gv/uqveOmll1hZWUke96EPfQhEidzyvZyonOtRWBB9x+usLNfw50j4Gq02xWPRfCGlcb07JKerybkT/UmyXCcIAvl8lqllY9tHfZLJxGJpqZyU1jzPSwKc0wxtrKsRhmHCeQZRzy5mLDi+Y3Ia/9OdwDBuP359Hk7bsJ+ffLsbBH5AfzBcmBx7/OL72Ns/ZHmpuvD5xHdQ5vFuz+Mu8PBlI/zip3/+1L95joesyid2OWLc7I24WDw5KXOaouDuXoNqubiQxczXmjVNJQyi6Zj5xa/Y+B8cNPGDACOdplDM0e32OXBb1IopquoanXYPx3XJlktIblQPP65O1505lXKhQLPTxZhF4kNzjCiKyJLENJUiNZ2SL+RJpVJ85SsGlQpcvRry2muvJRlIv99H07Ro0XGGXn9AvlJmtVpiYFm0JotbhBWB5NijDXWN3mCErqloipo0nvM5g0Ihx2AwIgxDBkMTURSSMcx0Sp+dt13Wmh28B6Nz3er0UBWFoRxlOEuGQXt/PxFSgoh6ZTAcYWTSaEpE2+43WoTtDnu5HPmcEfURbDsxfrGwkq5rCKKEY1uMJ9Ok3BR/R+bYolKp4jg2qqoRBj7urJ/z5y/u8B0PXyCjy4zHRxQ1mUwmeXyr2TihRngW9vYOk+wRSHoxx6/V49eod/gS8tJD0XOGY7r9Acv1KrblICty0gNIBNLmSrZvp0fQanXxg0gZ83Y7TefhdmPzsMhKfJxk8jSEQbSFf6c9khhnUdefhbtVEvxm4t3M4y4gzMpPV0o5dnYPFubE11br3OwOuZA3TjzP9nyqmVMIdU5BGIS4rke7219Iu8MgpNXqJk6i0WzR7w2RZCkR8IGor2DMpGcBzLFFqVTgeu8mQa/ErtdAU1VkWWbSH+D7PgPTRJ2VZmISPd8PyOd1vNCKlAXtqMG3nIuiUtPXuWWvsLJcw/F8RuM0H/xgyOc/L5BKRaWeWOlPkRVMc5xkIpVyifuv3sP1Vo9Gv0NG0snYFmNtZninE/LLS2xt75NO6Yymk2h5sVxkMDSxbZeskY6IFc0x0kzRz5w5tsiQh0iSyGRqIfaHaJpOqGtYb23h12sIgoCqKqyn0xwMRhyaJuv1KgcHUZlvdy8yzq7rJnV3z/PoiiJly2bj/mXM8QRzPEHXNCrlIp7v0+8PqVZLEVtsb4CsqRSLxcRgS7LEdGwhAXs72xhGmnarGbH01srs7R+STqn8v1+7xXuWFVrtDpcvbqDpGtZ0ROAHdDttxpNpQt1+HPsHh9SXqtG15Ps4rptcH/N9k6K+OK10IW+wPxwnrMwZccprt3ZnOjIjSoU8g8GIyTTqp8WvMxiaJ4x1t9dnNRVdv3eaPVSrJXZ2DgiC8MSSoet67B80bzs4EPjBiWOZTKKSWBgGyXiu+sAPMhiNKBUjpyfJ0pklp4ODJuVy8a4dB0TjwnfjPN5JeNd53CWulHLc7A6pl0soqrKw6FdO6ycW/wDGtpvobsP526pxWny8Xuv6Pu7MCIgC1GtVND0S9+naDpubBUYjh2436nXUyiVSGX2BW2lpqUy/N6Qwl5LHug6qImOk06QyesSZFARks7OLPtTpBJdJpTRkxeF6xwPP4kH5LQZOHkSJatHj5oFDtfoG2ew6o6GI67kMh9HM/v1X76HZ7OD7PlPLptnsoDkOxWyJRrODLEkopombSqGpKrv7DTQtouiAgHwu4uuSZzQuqqYTBj66rjEeT1iqVSAMqVZLNA7beJ6XaHvYthuVQ6pljBdegkKJkhigp7Ls7+1wqVZmZzhGVlMERoAoqRQKhaiGbxj0+0N+9/c+w1NPPRUZr3Ia/4WXcC5s4Loe1XKR7d0DSsU81WoJ1/UQBIFQFGbLllGWNJlY5IwMXuCTzWaY2haNWd+mViszGIxYXVnitRcaXCwFKEoUcMxnoKIkktK1E43k+VHUQiHHm9dv4bou91+9Z6HR22hEWuunZciCKDDxjhySZ1zkYrWGOTdJluUo2xmPJ4RhmEjJznNRzV+/fnDSecSlrhjxCHC5XDzV0SiKfFvHcdo47nBoIsvyqQZ8PJ2eb9jDENt2WF6uzXoyJ/twt0OpVLijTOidiHdOd+ZbAGEY8tbeIRlRwA/8BUcR14znYVs2tmVTyugYs5IWQCAIaLqBouoI/jHSJ2BlpY7teGj6URYjiSLFYpGLm2tsbqyRK0RRkIBAPmvgeTq+e3Q85kxCtlCu8NZwh4xjYB4j6ZvOmsQQ3fi24yRytcd1qwVNiwR1uj7O4SEXqiV8NZruarXauJ7NqzcPWF2p0+lERH4pTeOeKxcpFQuY46h8IwgCmxsrZDMZisViUnKTJBEpn6MoROd5baVO3jBIp3QK+Tzd3iDaRk6nsNNjGtIhh0qb7WmDer0aEfTN9lp8zydrZOj1ehQKOWrVErt7DfrDEY3lOp/85Cf53d/7LP/qX/0rOt0+rg8v/eVf8slPfpLOrVv8zu/8O1qtNn/6hS+yvb1LNldgZSWiIQcYTaIeTrlSRNci8S5pbi/Hdjz6gxGKHFGzSJJI1ogW9AAEUaLV6kb6LLPjjvTgjwz3pQsraIqKcsrgwGk4bjTvuXIBiAj85kvTS7XKCcfR6y1uYjuOw81bu2z3QkTfOjPDyWTSeJ6fiHwJgrAgwARRttababLM4/iOhjd7D1kW6bR7ieb4cZxGSBjjNM2NXM5AOqNPMd+/OY7RcAyCkDjuVEo/kz7krOXgGLr+dzPzeNd53AUEQSCdz6Lr2pFGBqdHcTHmo8aLhTzXu0N2hmOubU94bdfhVe8RvvSqx3M3AhpDhcZQ4cs34Ms3wAtCBMngRlPk5rDAtYaKi8bLuyGf/XKXG02RlZVl3ugaZDI53ugajKVV7r33XqZSjWt7AhPbZzO/ynKmSr/fpzcYcr07xHY8Ov1+IhWrSBKpTBrbccjns5jmmL29EXt7UeZgqBFvkdXts7GxjiI6uPn3RKSCQHfmmFJyKqn7lyvFaF+iXsWxLfI5AyMbLXp1+n3G5pjNjRWmY4swDEm5bqSGJwiMpzbNThdJEiNhLEmkVi3TH5hMnaMGfCVcjOharS6GES1exkMAgigk2/OSLFGr1XjmmWf4zu/8TnLZLJ/85Cf56Ec/Sq1W48UXX6RSqfCFL3yBSqXCSy+9xC/90i/x0Y9+FG3WQM9mM0iPPoT//NeS0VtFPjLyRkZHkkSKhRzVagkjE0kCy7JMfzik3+9TLhUQRSGZrml2upQK+QVD1On3qS9VsS17wZjuTGyud4cR39kcYlbXbrc/m3zKEAQhwdzW8o3+iCul3EIgMb/lXUvrhKLE2mqd1ZUlpGnj3K32QiF31GQXTjbKw5DblnsajVZi4D0vSAgGHcchDMIFip31teUke5rHzu7BmWWn00bn4Ww6kG63f2LS6uCckpkgnG9GW+0uW9t7Sfns7wreLVvdBfwgwPMDOo5FTlfpTmy6ln2m4zgOcTYee7GQJ51O8ycv9rmwpPPIZYN8Sub6wYTLtYCxpbLXcZBFAccHRRYpZ6OvajQUaQ9dHr6YwfFCZEXlgw+obDVtylkZRRZpDT1uHVo8cRlkMcON3h6XcutMBg1EUSSjKnQsGyuVxgJsz0OWJHrjKX4qcoqiKLE8t2B17dU3uXzPJcbDMWHgI0069MIyq6tL7O0f8uLrfS6tZCHo43nBQmTpuh6TqUU6pTM2x6TTOoIg4LgO/d4wkaBdXq4xGo2RJJHpJCqV7OxGNXBNU3H9Poe9Q+4trLEb9qjaJfJL2WQ73g8CtNn7um7EQBvLr5ZnIkG2ZfP4Y++HoB7nZAAAIABJREFUF17kYV1DqBr81//lj2JZh3zv934HW1t9VEVm9cEHuVLKJZF042CPTOZIuMh2HGSODNC8gY6b93FJqlot0e32qVfLmFMbRRJotrsLAk/1WplOt8+bjTH1QhTlSkA2kwZRSJh3Y1wp5aJBg4GT/Hy8NJIs350Srcd6IMBC6SanqwvBkDRt4GY276iB3e72Wa2XkdwRBC5+KpK43dk5IJNJJSWg44gVCGN0j/X7YsXCuPxTr1dP7Fyctv9yHhqNVvK+3W6fYiGPIAp4nnfiPJrmGEEUE8eezRoLZeeYoWA6tbFt+0RJcX55NJ3+m2X3/WbiXedxl7D8AEOV2eqPqGVSXEnf3nHEI35x1AchtmXy96/K6K3/QJh7CtsyuTy7ry7XAi7XZF7Z32czm6ZkgDkcoekKBSPg71+VabUOUX2fg30luqFysDtqIPkiWjrP1cqYbsejUoG23+YS66yt1rneHVJWFYy5seGt7X3W5qIq2/NPbOYKqs7+aELKmmL5CjL7FHIZfFNkoyzzxsGUcbfHbjvEkiw2NtaT5nu3N6BUzDOeTFEVmW4vIgkEEnGomO8oCAJEQcTxXfq9IZ7nc/HCGo3DNo2GRdEo0mlPUXyF/EZkyOeNj+35C/T2RibNdm9AoZil2xomFOS2kUZ++TXAI8AET6DVnZLSNWq1MmEQRkZ0tX6CNXdre5/NtWV47OFk8zzWLk+2+XMGhVyWbn+QMAWUSgXG5hhdV5EkiUw6xWGzjWM7SLKEJEuMHZmLVQFR1Gm1O1E4T1S2vN7qoUynVGbGNK/r5GclkfOy37MMayynq2kqmVQqibYvF7J0Jhbl2Kkccxxi4KJ2vkKIgF19gpu3drlSFbmYHuCIS/gzydWYRXd15XE63f4d04O4Zyg6zhv1fPbsbOhOMO+M51/3tOzFMDIYRmaBXdi27IhWRtOiIGU4RlXlc5caV+rR5/8bYej9FsAdl60EQZAEQXhREIR/P/v5VwVBuCkIwldn/z1yznNzgiDsCYLwv8/97v2CIHxdEITrgiD8G+FuKC6/yVhKp9ksZEndwQWwOzAZOnbS7zgNw+FRqSKuq8aP79kuhipTrxQppDMMLCvar0ilmB6bLKmWi6R0fYFraTJb4oufd6WUW3AcwAntkJ3hODmGphk9X0lrKKKAqijkZrX9UJryVquGnN9ks3SVcukqO937uXrflUSpJpvNsL5aJ5vNoCoypbLK2loRbcaLlM9mUWd1fT8YRwttjkutWsZ23EQQyzAMUrrGXt8nlzPOLCHMOw6AvYOIEqbZ7KLrGuVyiVarS6vTQ3jfe+GF63iuimkJ2I5DbTa5FoYhV0q5qMw4sRk5kWzu1vY+hXyW5rNvYH7hVaavtGl89ivYr+wnjeN0Smc8mXJw2KJUyDMyJ6RTOoEf4No2YRhSrRQ5bLZRFYUgDMlljYgensihttqdRC/leneIQ7Tg+KlPfYr/9eP/BkXVESUVPZVFlFQeXF1FlFREScUcW2h6Bj8QUdQ0ippGT2VR1DR/9KlPAdFU0urKUiQpu1RZKNMIooDZ7tKf2rRT78U7PFI91VvPIrgmVvVJ7OoT6K1nuc9o4KdqOIWrC+c+lmLWR69HNP9zyphnIZ3Wz1Ux3NreJ/ADsrnMCdnaO4XneW9Lk3z+HGm6RhCEye+yuQztbv/UkloMcUZOatuLx3285/ROwd24v/8WeBWY/2Z/NgzDf3cHz/0XwJ8f+93/Afwk8Czwh8D3AH90F8fztw5JFO64RBVjLW8sOI6mOcULgmQcElhYHswa6cRgXynl2B8e3XCCKCxEmzdd78Tiku/7rCzXGJsT1Gw62p8QwDGnVBV5pjURRb3jyZQwCBKqktFoTDBjiL3eHVJNq3SnblJbd3t9ZF2j0eyQT8FWQyKVAseFdhvW12EyYbY9LNBu9ZLGe6wnsrXVTBYbNzdWaDRaiKLIUrWMyJRyqcDOXoPDZhs/CJJ5fPXBH6RWK1OrRZxFd7rjEJc2lpdrHBw0keWIgvzgoMnEcvDvuYTx1VcwHnsYIxM9VpJEdmbyrtW0Sl7XI36yeo1Go4n5uZeR/vh1TCKuVSHYpid+newPP47x+BqDoUmtXGJgmozGE/I5I+GwCkUhojwv5JIyneu6BGFAx4y+h/F4vOA4DMdOsquf/qmf5Hd/7zP82q/9XxSLRXq9aIv9Yx/7GJ/+9KcpFotsbW0xmUyo1+u4rouiKDzyyCM8+uijPPTQQ2dS6MwjHle2UmkUu8Jq6xoGQ14W3sMVrZAcG9L9SIKA3z+ij8mpMrW5JT2ncBW1/TxG5bE7+s5iwsXj0FvPcqVUR+o+h1V5IglA4PRofp43K172BJKdnLeLuHxmHXMCt9toD/yAfn+IJInc3NpFliTW15bPdZbfyrgj5yEIwhrw/cC/BP67u3kDQRDeDywBfww8NvvdMpALw/CvZj//OvAM3+LO424JLwM/esKSIi0Yu7OyENuyk0yiGkQOInMGBTvAxWL2RLmiXCliji3aIVxWNXbtWxBE6m/NZofV1SyCDwetCYZhoEgCjWZnloab5PP5hdezvZDuLMJbW63TH4zQBREflStX7uHWLej1Qz74Qbh1S+BDH+zhOR7dwYBKuUiz3SWTTiV7JKoi4/l+UkrxgwBJlhkMRoynU1ZX0miamkz4+B/8MDnfYNQcM51aqKqC60Ujyxtry2du/B6viQd+wPJSla1Z/b9aLSGLEt2+RVgu4j//NYKHH2D/oEmpmCebSXNw2MIfT8jPXmfHnLD8Wp/RH78OTnR88+8++s3nMIIA9eFa1OwH1HwWh2isNTWLViVJZG//kNWVJRzbwZxlh9ttj4q66Dhi/jCICAVTqRQf+9g/ZDqdsrKyys2bb1GtVtB1nQ9/+ClWVlYxzSGtVptqtZL8H+D69Tf57u/+bqzp6Mxrah6x4NXWdEo7v0QvvcQVrxBNFqoyRV09Km3N4WZvdMKYx9xZd4u4ROamN7CqT9Jq97AdnY3WC+iCm4izSTM+sfnm/HwJaXNjJSkfViulxEE1mx3SaX2B+LGQz7HfaJ5o/se9Dc/zcV2PleVa1Fyf8cOZs+m5swYMxJnkgGFkTh3dDfzz6Y++1XCnmcfHgX8KHD8r/1IQhP8B+FPgn4VhuOCKhWgM4X8B/nPgQ3N/WgV2537enf3u7xxGozFtL2AwMFk7ZYEwkI4itG5vkNSFY6Noe1HtvaSplDInb9QLeYPdY69tZHSk/oC/FiJJ01gfOwzhYN8kCEMyM9nXcFbmGU+mBEHAYDBYaKbG9CrD0Qjb8cjNIsrQq2BbJunUiOffHPN+L41pdpm2JbJLGURBwPd8apUSI3OcSKnGWU68b5LSNaaWzcTzo43uwYjp1Iqykc0yTuiioCyUGXJZI9kFiAn2DpvthZq6P38jBiHtTg9VVcjnjMixLC/RaHXQNZV2Ok210+PwoImmqViWTX8wZLlepXHYZjQak1MVxi/vMfzN5xDOqMnj+pi//TxB6jE2P/gAo/EERVHo9gaIosRgaCY9kTgKLpcKyIocGZxbDRQpOu7Yccwz0NqTafJvI6PjOpMjGhRnEsnDOhM0VU5+P28A11brkeMIIup9iCYIwzA8c4lPlEQuri7RkjtUvcgwr6a0c7fHM4p0xND8NiFNmyjmW7jGpQX1Tmk2ORdQxyLKRgIpjVN6iPFkei6T7jzRqG3ZWLZzYhIs/vtpxx73Q+afM+8EHMclCPxzp9MyqdSZ5+7t0LB8M3Fb5yEIwg8AzTAMvyIIwnfN/enngAagAr8I/PfAPz/29H8M/GEYhjvHWhqnhYunxvWCIPwkUXkL4BfDMPzF2x3z/18IgiDpScT16Xkis5gWQ0BAFAWCMDJsfirNxXzEqAssRPah70WlpLRMupjjZm+EH4bUjTSGKlMzUtRIYTse3bF1woHIkkg1pR9lM5PIf3fT+2TEFHmzwEAcEQbhLM2PIq4gjI4/cCOqdgBP9ElJp5cMjJREJqUlTm0ySjPpDfkch1TIst845P6r99Bp9yItDM9DUgKGQ5PByESajbJqmoqAQKGYw3Y8xpMptUoJVVXZb0Sb1ooSGb/rndObwPM3mSBG53t5ubYwQSPOZySiQD5nJFvEkiQRhCG+5+OKkTMz77nE8ptv0d1cx3Yc1lbqbO8eAAHmzPlNpj6D23XmBAF55BCEIUYmTbvTS5rpAKqioBlqEmWKkkixkAdRpl7KoHjWQsZRnvVRALiNcTlvk3uBokQUFjiUwlmGHC/qnQZjZNAYtqkvVWi2ot2c0yjdAUopndbIPOVV7gyib0Hg8bJ0P0yBcZ/llEq3NzjR/E8yj2mTq5ldWkEdON15zNPID0cm63/Duua1aum23FfHR4DfybiTzOMDwA8JgvB9RIzNOUEQPhmGYayhawuC8CvAz5zy3G8HPigIwj8GDEAVBMEE/jWRPkyMNeDUDZyZs/imOYx5iJK40Kg9TxI0xs7uAdLMccZlpvmy1VVAMAwqmRSyJCb9DDgqb10p5dBUmR1zcmr20e10yRDV9QeWxevTV3kodz+t/Q5r63d+gxSKuROLhDEyks6hOaGoykh2k+5Up9s7pDZJUS+4XFrdIAxCVEfBUhwKuRxqCiRRZ2Sa9Gc7JXDE/9RqtREEAdcP0USB1ZWlhXJTRYjOQca2WKpVeKs7QHfsE7xfMSq1Ii+PvsY9ynsQj83eW7ZDXs8yHVtJCdEw0ohitNxnji0OalWWt3YYXbnIeGojyxJTz2Vzucb17hB5YnG7da8wCJGIRnHDIGLajSnf0ykdx3XJ5wz2G00KhUKUJSoqIPP+qyt8/esdKmJUr7dtd6HceTseuv29xsLU3MJxEZ7odZjmGHM8RRIjGhcjfZJCJy4FpVIa/ozifLleYTQcn+o44i1vQV4MQialx0nPWHpvN/ardr/KnnAfOf2odzJ1PYRslqY5Yez6ZBRpoa/ip2qY5Mnq6kI2krz/xFq4d+ed5Nb2PqVCHj2l0W5335ZoVOy472aSap73am//kMo7bAv9tp8yDMOfI8oymGUePxOG4Y8KgrAchuHBbErqGeDlU577j+J/C4LwXwGPhWH4z2Y/jwRBeBL4MvBfAP/bN/5x/nZxJ/TJlWKRPduhaU4ZOu6JSFpqOazkMpiOh3FK1HildJSNnNest0KH53ovkhFTSZlqdeXIcdiWzWBoLvBxzZeCRqMxleoSmm7g2hOCMJI+zeeytDs9ivkcZgiGrpEqbCJKY1RVI7g14erlOgPLYqc/wuoPKRcjZlm/ExxlaHN2bzKdsLUdbbcLgsBoOKTX7WLrKS7Vo/LWzmy/ol4u4adV3hqYEZ36HAaWlThbzw+QJZkHs5FgU6W8eCPGTj7WxtA0lXq1nJRvjIyOrtVwyiWyr75O+MgD6LUyYRidm7qmMhCF2xo+IQxxPA8xCPBdj7XZrko6pUfa354/o1mR6ff7GJk6f/mXCpVKpCL4fd93L9PpBGs6xbLtBWoL4TaN3rMcR5zlDEdjvJnuSRAE9Gdyr/VaGVmWKRZzuK7HeMbZtbZapzJTHpxvODdbXdbOaA7Hhlc5xuXmBwFW9UlE30JwhvipRQMdl6ms8mNMSo/TG5hcmXMOKUVeKM16fpBkaJOJhWUdCXLNl7kU8xbStAFzvzuOeacyP1iRTutn6qsfR5zxxY5j/nydhfF4guu6ERuDrjEcjZPr8Z2Ab2TY+DcEQagSlaC+CvwUgCAIjwE/FYbhj9/m+T8N/CqQImqUf2s3y98mUhmdK7NsocadkSPGGcdpGcuVUg7b81EEAVESESSDCxcNJvaQi+EaYRByc3ufYjYSS/I8n6XlTaZuhlotijo13cC2TPqjDK3DVxIOJNsy+b3/+/9hZ2eHRx55hJ2dHTqdDu12m6eeeoqnn36az3zmMzz99If5yn/4S77/e59m69YEzCl5I0Ve1xlZIoN0tA9T11Q6nTbFiwVy/sl+z+FhhyDwWapVaHX7rM+cQ+OwnUz7xP2X1sRJyAVjzGdp3amd9Geiz6gllOmnwbYdtnYPyOcMNEVlMp1SLOYZmCZVYNCPDGvczN7cWGGgC3ekNZFbr9L3fbKzfYxgRv2ytrxEMwhmBI5SstAoGje42QvQqh6Bv4aDSC5rkM9nFzbO3666teu6rNRriJLIwUEzOYdxdqbpWpKRKEq0q5Ca/RwbxdgQHhxEjeTxeEK702e5Xj11OqpnORE/mqbihyGeHzlTPwhw/DR1M5reimn8ncpjiUN565ydlRiyJCaBVcq2qNerUfnv+Gc3LuAaFyJtkuqTd0TSOJ953K187XF26vMQBCH5fHahenGu7MO3GO7KeYRh+EXgi7N//4MzHvM8cMJxhGH4q0TOYv5xD97N+/9dhe35pGQFCBOt5LjnESO+mbpji7Qu8PzwGgAb0uMAbLfgiXsNNB0kQ6HjBty/EUVNf/HKkLWSxNKMK+va9oTOKGCtNOHBBx/Ecx38OY6t6XTKZDIhnU7T6UQ7I08//TRvvPEGzzzzDJPJmO/9nqfYbx09J4r8o5syVwJVcJng4to5mq2AJjOH6PuohKiw0Nh0VfWoTDfTdJiXclVMEz9nnEkyJx+76Xb3GqyvLZ8pNRqzE1u2g+06TG0bw3UjJbf3vofC11+j++QaaSIj2qKD+uEVAn9K8BvXTm2ah4pI5h8+RjockF2LMqBY1973g4gGftZ3chwXx4mmxvIZlcE0YCk1TV7rdrXz+X7D7XQmVFVN/j5vCAUxWoA7zaDGzmQysUjpGo7joOlaos2SyaQXonLX9Wi3uwiCQL1evYOR9vtn7AY+O8MxNctBFAUa5vS2z50/Xj8ME/13QRTOjPg76YcozxzI3WB5ubawHHg71JcqCSvBWUzAMe2KYdxZMPmtinf+muPfJsKIgqDT7VEuFVEVOZEWVRWZ/UaL9ZU6ASHhGaqDgR8ytS2WpOgCs6pPsjPX27jRH3G5kF0wBnF55s3BdXrBCJyjCSpB0vDcMZu1HNe2o1n7akHFHNvYlsnQ0XnkQpZsRsALQr5+c4Lrh3zwgegG/YtXhtxbMhOm3ae//3v5T/6jjyQRr6aqpFMB3mCLC2sF/PY1VCWDr5V5ZbsdHaCRQp4dJ05A3s+Rw6DV7nG5lJ01nqNlxNhhXO8OMfcOUYwMWS0qSewOTNwgpDu26E6mSO4YX8/R3jukUsxz6PpcmbGUxhvCoijOfn+6wanVIm1yBIH6UmWB3E6Ld1ZyBr4fJMuVpqViAJO3bLS6gt1wSV+aReLfc5mpKaF99uvJiIcwI3PM/8h3cGtNZGtrix9So6wom8vxuc99nh/4ge9DllUymQzj8ZiDg/1E9reaL9EZ7nP/1Xvw/IDUsensMAhxnMhRx/2T8WSaZAeNZoda+eTSmygJ9AajEwJLg5kuiuO4EXXMZIosS6eWaFK6RuOwFe3HnLMboijy2+oVaLLElVKO/miMkU6xmlJv+5z5YYjVlEbDcojDCe2MHZFMJo2VeTLJQI5PZZ0nMTueTs90HoeHHRRFShy47wfJ6yiyxObGCr3eEFEUMMcTioV80ufQiLjY3s7C4rcC3nUedwMh0jI+fpHJipKw08p3oBaYyugI3W2c0kMMLAtJEFjPReI8xx0HwGvTHYSpSUWqUAprC4bSsgcogpDQm4iCSBBapAQPkBHdPtdv7AARLfr9s4Fo2zIRBZHHL4v0e0fvZagyQRhQFfeRnD6D1Ps4bE/JBxN6Qohh1DC0o3LRhx6tz17PxhqM0edoq23b5rDVIZ+NJp0Gw1GixZ7yLNL5DHIokJ2VnwopPcm2ShmdMChwoz8iX8jTbrXRFRnIJTxEMQ6P7c34wWIUbWSzGBl9YY5+NBqTSafYXFtOyleDGQFfPp/FKxcThxH/P0bxex6gW89TnngQ+PQHI5bes8EXdq7xzCM/xJe+9CUkSeE3fuM3WF1dxTRNfvEXP8HP/uzPRpT00wmHh002NjYYDkd0uz3sXg9Y49bARJ9OFq6xxmGL5eVaEsG22r2FhbT5fZf5LOR4FN7t9lEUZaZBEhlpZTYmHJ7ST+m0exSL+WRazLbshNvrPJw3tQVR5D0YmNiOgyJHezvx502ljq4t1/VOjvyGYdJ3cl2PdqdLppDH8wMODk5n4p2HVY0cyNJSlIEEM/lg6Zz+Zex856f5Ypw1FgwkxxmfC8tyTlDAl99hTfJ5vOs87gL+jO8orynJOC2QOI71tTufbAql6OZtTZzEGZwWPffdLgImG8IG9Vz5xILhcTqOeAR3bE7YbxyyVK2wUl+KJqkcb6EUNrUdzOEIczKlUMzRb+5Q1cbgTDDzD6HlZZzRmHany9J9q9TFNDdv7dJqQcRsc7RrMRyNcRx7wXmkUzrZrIEoeYxmBH5BGDAQJBAUgv6IIAjpzii7XcOIzoEwQfABMX1ETigeTWnNu1bb88mpSnJedEkkqy1e1nHfJJ6W29repz8Yks1mGAxGlIp5ZFFC4qjpfKhp1J/dpbW2giRJiIKQsL3u2ib+vTUsVaFmpBLKhR96+CI//z/+cz7ykY/Qbkc0FXt7eyiKguu6tNstfuVXfhXXdfmZn/knQKSV/fu///v89E/9JHutLiVJpLRax/M8THPCZGotGM/RaEy1Ulwo3cQlJVgsdx0vl4zMaNs9djyOc7S4J4gCjcP2QpYSf95+Pzq3zVb3ttf4eDxJjGXcF4GoKR8fo6qqszp/SLc7iPTsmx0EQaBQyDGdWuRyRjKyDXOOUBCYTi2CICCTSbO+tszBQZNbjseVteXkWE/D7l6DUrHAYXAPm61nueldYnk56gXdSdZUr1dPzRTO2tg/3nPz/JOLkqIkJq8ZlzjfKXjXedwF5ulJ+tOIr6qoqZRnxuksSuizYDseun0+P88b5haPF9+XROxnodPuJTf7G3/0HBUxxQVdg2YLKyXC4zka5mTBQWmqjF7SWA1fxw4KFGrrXHvzOq6XJTNtUCoWyWYzPFrp8Ppuhs2NNMXyfbzxWmS0soDjB0jCBNtxEESRYI4u3Q9C9g+aXFgrIMkSvh+RHhYJSeWzaOri9BRE5ax7CrPzKExoNCJeqOM8XjF2hmOulHInhhH652zrynLkDAaDEY7jousa/eGQtZlj2dxYYXVlCc9Qqb6xTWdjjVwhlwhrxZQzQ8dl2HWTnYxsFn70R/9Romv+zMYmk/uuRKysgoBjW3zvj/wIV0o5up02B+0++0Odn/un/4RWp0clm4lKabMIt1DInRiHTZrdcz2K21GNxDjuTPxgsW9zvLzleR6dzoBqpYhtu+dH2USjv51ZuTPuiZw1reT7AamURiadJpvLkM1l6LR7KIqMopwcrohrhLZlo6nqwueP5ZjjAMI8YyE3dkTptI5FmYutZ7G4u1KbdEqzPSbNjEfRV1dqyHLEpLC3f8jSbJqtVj39/DkzeeZ30qQVvOs83jYKKY321Kac0emMLXq2w2o2fUdkiQCC77A/nmLIZ9OP7I2O6vNxRnE73Pj1L5L74i0cwImbyGHIuO8gfdv6wmPV7kt42hJW9UlGozE7u28m1BgxZHMbq/okm7Ofl6oCxcJsqawfks0I9DuRyNPqyhK9G5HxiBlmZVlie68f1fJTOq7nMZBkrqgyQjAhFBeNiy6J3NrtR4YuhPpS9PeBNbd/MhddV9Mna+Su6yUjwjFiFtRqtYQoCFQqpUjlr9un2xsgS1LST4jlbyvlIgHblJot5FoZRZJodXonJFIHlkV+ZtTX15ZhrgQ0nVo4rke9XmNn5rxfeavN1A4YTFWWUyO6/Wj/I9YLgWipLyT8hra0z0LgBwmjwHGMx5PE4MuyjGXbiJJ4W8cBMznaQuGOxlvbnS7ra8sLvQRnTuP+OIx09JpnOcrhaMyVuYzgPJbheIIqLmHdTRO9VCowGIxIp1ML+xzHv6fRcIwsSsiSzM5eA1GIBMEc1zvhpMMwZNLd57KyDeHbnan728e7zuNuEELDnGDO0v344ixndMoZnWZ/REcQqGqLTbvjF/z17pAH/QmybXKePMyed5g0xuOo+7QbYnevwUq9xo1f/yKZL95KeJfmMfzNL5OdTNn7tjVWV5cWbpprr77JPVcuLjgOc2zhey4Vp83BSMdxPTRNZXPTQFVgNA4pF1wcV6U/HJLSNZpyB2XGp6vlNBB1zDlZUxPA91mPKU7mHEcc8S9ns3RmzfCbt3YTVt2R7ZGfKxfH0bkunryEe70BFy+ssbW9j5FOIYgCpVKB6hytemxAqtUSh4cdXM+lPzApl0v4noskKxw229RntOvey68xXKlHsreTKVdKhSTSbU2chfIjYqRA5wNDWQFDWfjuGn2P95Wa7IUp1tdWosGLmapd1kgnuhexM1tAEC44T9uyURTljqkt4uxJECVcN/p3s9lBVRUKhdwJwx9zQsmyTC5nnDu6ejtiwHmcNkJ9Htnl7abPcseee6WUozMTXzrOvzV//Fblibt2IPFobeOwnRBPxguhsROJs6njODzsoLeexdcqhFJkFzZLGn6qjsUKCH98x8fxzca7zuNuIJD0OU5DrZDF9nxa4+mpabPteOyYR+yjmXRqptF9Es/1XqQ6qsLJys4JrK3WeeOPniN3huMAENwA77NfJ7deRlefZVp+AoHIcRzPNhqHbQzDYMl5hVfHa6hqyObGSiRNurXFy3tRhHjfUsSNJEsyiqLQb4a090aMBvFnOj2S3No5aRQ/+PA9vHVTRC83kps9dgCbGytYx8pQ9XqVpjklf8p0TTyam7CozqjUNU1dYNmNlyBr1RK9/oBczsA0o4WtZrtLpViM3v/Rh/BfeIml+gq+7zEcDnnp669y6dJFcrkcf/3Xz/OBD3yAwaCHLKtMpxNyuRzm1fu4N5fjD/7gD7nyPU8BsNeK9MwtX2JjtU4mnUoWImMEQYg5trCn00hgaWaUd/cahGFIrVJgyhImAAAf1ElEQVRKApI7LVnFSM0xFEwnE0xznJyv6dRGUaSk/NpqdSkUFkWmbo0qXB94fPh9RSaWjyK6KKqOH4R4fkiv06RaLs225mE09bGcgIIhIwkBnVFAVrV47kbAB+7PMbai71UQIFvIMLF9JFEkl5YWCBxvpwGu6drCsEC/P6RciCj1TyNvTCAIWNUn0Vpfxq4+sfCnmLzyLMxnEKY5RpspVw5HYyrl4qkOb1N8k9enG2xWb78H8q2Od53H3yB2ByaWH6BLIr2JTUGP5uvjDfFqWmU9l6F12OJQLLPCa/iqitS6hY+KIMk4pYd4rvci96Wu0h20b7uktLvXYDgccSFdYHi77WdCONhie/1BnIPDWWMyy8FB84iHK6Zfd3v4UmRUHMdlZ/cARZHZ7h45J98P0DWNkSjy8pvRjS6PLP7ek8sLNPMAOzv7rK+vnLq498rWiGxGIJWCa9dH5FJjKimPixdWkn2MalpNCCB39xrUlyoMHXdhMfA0bG3vk88ZrK/WT0Tna8tL2I6D4zi4rs/efpNyOTLM8TFm7TSj8YTsYw/ziU98gp/8iR/n05/+bX7iJ36c3/mdSI3g6R/4AT73uc+xsrLCcDjk1q1bAPzHH/4w3U6b/f2oFBUGIa/tOCynRqxcfgCvv8PEUtBUJdHwhohKZNAfUCkXKOtFut0+KU2nWo403xGPSp2j0Zhub3BHS2nHIQpCwia7tb2PLEtIkpQYxfnGcMxI+8R9V9hq2fzJi9GI3ocejrKp5980eeK+LK+3UlSr0XX4Z18bYHsB3/1oHkEQ+PLrE1wv4O89kEcShzz/psl7N9P4IbzVsLBm/bJLdZ29jk/5zpa7EziOg+N4+IGPJJ5dDj4NduVx1O5LOKWHFvped4r4PKJEI83z5b8YcYazecrz34kQbseX8y6O8MgVI/yz3/r55Od5bqup653od5iOR8OckJtN5WxtR4JB8V7CaDTGsR0EUUgkSZ8ffo17jU0KSunU9wGSGXXP83jjzZvcf/UeJn/+BsNf/yuwz6G+ViWEjz7E0g8+CkRZRyYTiTSpikK+kGVvv0k6pbPOG1jVJ5Mbqd8bMrUsrh1EN/iHHq3zpy8cRcy5lMi3Xa3RebXLNOcgCMKCIYpx2hJXGIT89VdyPPJowF987cbCa4bmZb79SRvfd/D8gFuDaJx2nmU4Pq/Hyx6x44ibzmdtF8dSt/PHFS94SZJIPpclk05FpIqKyvT1N1DTGVhZxnU9dF0HAkBkZFkokga+jbl1i+KVK9jWBEWR+dMXGiynRgltTM25xjD/fhqNaMomJhuMqUGOf5ZSMc9gOPob64N02r0kOh6ZE9RzdjU8L6JUEQSRMAy4tifw8KUMrm0iihJhGDC0NbKqlYzqxlQ4YRjOHhPbmihQiV9LFCWCwEcQIroaWdFw7Mmpx3EWYmGomLQxxs3eiIvF88eLR8MxoiSQ1UWk8S5u7spdvfdZmL9vY8dxHusBwD/42M/z4g3zHbFm/s7iAP4WQzwrbzqnl2da42hjOI6OVUVmMFzUUihXipRKBURJ5Pnh17gvdZV6/42Fx8RqgDErb0iIZHcSxyGb28jmDsIdNNuyOYNOu8fW9j7ra7OyThgytW16vWGkeBeEuLmolJVOpxgMRuRyBoIo8r7LUfkgdhyqIrB5Ic23XZ1JbAY+a6t1VleWyBtR6W5+UmzlFON0oz/i8W8boUpjPvRonaVCdF6H04BKBa7v9dhrTRA56htkpGi0dG8/Ugo87jgCP2BzbZl2ALuzJvRZfYH1tWXyOWOhx+A5HpsbK6ytRoJK/cGQg0YL17EI11ZoKxLdTpvPfOYzfPazn+XZZ5/D9z3+7POfZ9Q75POf/zzTVIrAd1AUmT//UpaLy1nqS9UFSopup4uuaaizwGM6V8Z03aNAoFwu4bpH+xC2ZbO718B2FoOFVqt7eq/kGFzXw/X9hKZ8c2Pl3Ax3bz9ycOFscOP+1RDXjhz5cDjE93z6zZtAtNfQ7fYJZ9ksgGVZhGFAGAbs7x8uvFas/x6GAUHg37XjAPB971Sq84xyfgayu9cgm8uQyaQJJB3Bu/v3nke8zAlRb2Rv/3Chp7K+tnzqTs07Ee+Wre4GYVSqEQQBkRDLdugPTCzbZrVeW9CQuNkbIkkSm9k05tii0+kmFNDDoZmUYGKa6OtCdONlFZXrzgWM5g6VdIjgT9hIQ2haFF0ByXZYLQjs7jd54L5NQuBGV6Z+4SohO9yOAUmqGBG/kOMw9iNDJUlSsiQ1mVqIooCvRdGRrqm4rkunN8DzPF6ZTVNdWslysR4Z7LPEreIjseZuqNsxjtqWzYOXSjxIxMH1la9AJgWv7Qx5befoffopi1Ihg4Rz9ovNour0OX2qGK7rRRlKENJsd5OxSdt1SGl6xAqwVI70RiybSrnAv/if/md+4Rd+gY9//OOk02n+7M++CEB9eZWHPvABGJu8vjNkqWjw8KM+2XQWSVzs3RjlDQByaQkhdCnVLpA3JGw3IJsSGZg+eUNCkDMMJz5goaeyXG+KLFXzIImMnIBqXmFg+rza9vjO5TyeMyWTib6fseUl1DGyKGC5PqIK66sZHMeKdtluU/I8a8wUIhEkw8hQmdtyL5UK9HpDVFUmk0lz0GglGcHdjrTP43ZULMv1xUx36HinDuMGfkB/MDyRxTmlh05toJ9WhoopYuYpc1R1cfrvsnLzxGvdrvn/TsG7zuMuEATB3Jy3gK5Fy07TqbWwWX6zO6Sey2DMmrmB76MqchL55vNZnHaXZj9kY22Zvx58lYyY4qpxL6IkRop9vSGeMafoZ9kIuZAty2PaanPPlauEHJEIAmR/+HFGv/Xc6U1zVSL7w4+jPbBCjWhUMiPp2NioiszInCBJIoV8ltDuY1s206nNaDxG1zTW1iMjV6uHOP4Az85zvXtAWpZOHZcFIrbcXg9FOWpqt1pdLNuOshvDIDe3OxPd0CZLsyawbZmsr43IGpnEUYVByM3DCd3+mJsHUSZ2vXFUPrtvPctaNUMQBoizxLpUKpzbO+p2+zhuRNVhWTau56GrKlM7GrPNZQ3avR6FfI58PoskK/iezy/8wi/QONjjkUce4bu+67uASEK2cbDH5eVVGq0yX/1LiYefgS99CR7/tiPH4boeYu3byYYW15sioPLijQnf9WCal7cslgoqO60JpuWjKQKDsY+uinzgahrf93lwM0Nr4OL4keN4eWuM44bcu5piOPEppRW+9PKAUlaeZXI+w7HN1I0MVyWts+/6rOZChDtgh06lIqLJYiFHGEQ7InE50J5l3vOR/3BoLmyZq3NBw+1kDM5DGIYR/9gxxCUyVVUXvutYh76S0ijMH9/IPLMJb5UfQ5o08NN1CEP6g9GJfZtOu4c0G7P3z9gpUtovRBT0czDNMZ7nn0pn/07Duz2Pu8BDlzLhr//bn2FzVkON+fh9/8ipnDZf7roeQXikVR43j2/2RrS4TkWqoJkqYRgiiSL1pWqi4R037fq9IbKqstNocfVytK8xHVsL0zMA489fixzIfCQZhGT/s8fJPH0/EDkiVVW5tb1HpVwkldIIQxJ9ZdEbYbpKtNQ3i5IESabfvQTA+98f8pWvCDz4wIibvRE530sWFBuvtKg/sEjhcBrOqkUHfiQgVThGbxGP2M7XkePsLe4BvXVw5FBi2pSmOaVmpM5VmIunihRFJgxCev3BguLc1LLJZNJJ1hRvBPuBiCLBL/3yrwKQSqWYTqekUimW7vsg3/+Be7h2DSzrBa7ed2Uh4tza3udyyaMv1CJ1RlEm8B1EQcT3/Zlg1YzOfsZ+O7UdUpp69HtJRVU1vvTygHvLJqVidMyuT5LhJLomc68PoOoZrl9/8xvun5yVCZxFUDgajtF09Uyd8tvhrAko0xwfNa05yYYbBiE3+qM7IGyM4B2+RNsrU189W+C03x9SC24RakWkyd6CVonSfoFp7kFEKbpm4u/MNCfnOo53Us/j3czjLiAIAm4YJg4i5qmRJJHJxOJwPD2hOQFRqcZ1PbrdPlPLZn19xlHEddJhiZKfx1d9XN+nmM8yntpJNBcjCANM0+SeC6vYno8mSyccB0Dm6fuRVwv47SMlN6lioD1wdCPfuLnNPVcuIooiYSGkKwxwtiJG2ZCQugGFpRyEaaZji73JlO6tDZ54MqTXD/mLvziKVFcyKYaDqJwU0WOfHYzMC+UcdxzxzS5KYuI4fN9Dmt18a6t1Wq3uYtnLNLnuB2huVLq6tJzh0nKG519t8qcvNPjQo3WGjouz3TtV62IwGBGGIdVqCduyOWh3yeZypDQd1/WQJYlub4AfBAtN7MnUIgxCdF1Fmikv/tAP/SCf+MQv8+M//t/wiU/8Mo9/93djXbuGZVlsbKwnEXPs8Dc3VnCDkHLnOUz1/ShKkHzP+43mgoEMwmCmBCkvLIsGvoM1dXjsAohSLvnb7u6R4U4e7zsLP1vTUUIJ/43grEzgrOmvbC7DZGKd6jxarS6iICTOSFVVDGORuVc/45iNYyWl5eVa0uSHqFQUZyHnOZBEoKl8PxvDa9zYEVlfP73BXXevJSUpN7OOaPdQh68D0f6ILAjJkEacBf9dyDhivJt53AUeuWKEX/jUz3OjP6SW1ulMrIUOw5VSjn5/iJFOL5SxWq0uQRASBD5BGKKrKjekWyyNqsiyQhiGuOkMF4vZE/xCcHIX4+beIRdX73yMMEZ8M715/RapVJQxaTkN3/FRBRlJEhmZJveldrHLD+EhMpx4jAcmruvxVldOIvoY17tDcp6b7Ar8+R+8yeVHspFBESJW1jiKP+2zQZRtxFrdd7LsFhvgvVaXqSRjODb5bDbZ/A78EMdzef7GmPdfSp96w8Yb3L4fCVYpiowyU78LCdE1lUIhl0w5WZYdaXKs1rl5a5d8zqBUKvC7v/dZnnnmGb741X1EPO5dTzOVZC6+dZO3MilSGYMw7r3oOuZkyvpqnb2DQ9bXlvGGPQQtzUG7d2YWcCfCQsfhuh624y3o0R/HN5vR9e18LsdxTvQV5l8vmC3qKYqcZCPHaflv50Bi/P5n/xCA//Q7ajiVxwAQRQlJVpD2vnjX9O53gnczj7+j8IOQvZFJiYCcrpLTVUzHTXobEEUWnnNyXDYkpFTMo6oqfz34KkujJbJGGj8IkUQBQv//a+9MY+M4zwP8fLOzJ/fgKZKitCQlWWot2ZbiQ2lTn5UMOKmD1m7QNnEKAzba/EiBFEjtGg3guDCCNgiaoA0Cw/njOG4doy2aIk1aR04iNQ1sx5ItWbZk2ZQoUZR47pJ7HzM7X3/M7HCXXB7LmFqp/R5gwdnlzM58787MO997MpJM4zcMEHmQ9pPUqdMf0NEeY2Ymic/nRVqS4YFeRscuM9zEhWdVrDrFUaXNEyBtZMlXivh9XgbjWyibID3gwSIa0hFGCL/PxzmnbtHIxDQ7nJ7hoVAIS/dgVSwMw2BXf4y+gca+hUaKA1i2/EUqlcGne90ZVrUAne7TuTg+gYhE2BELk8nkCAb9HDp6jnv272FiehbQ2LU1QtaA7Exu6U59YTpjEZIzl91iie3tbSQSSfw+r1vyPRYN253qSiVCTptWTdMwzQqBYIQHH/gkp06fYfydN/B6vVw/cA9HfvB9Znfv5swbR12FnUgkePDBB/F4NGYTc264ph7twD/zOlsG9te1Mp2ft8NdU/OZdeVw2DWi9GV9PcnkPPlC0S2HouueVfMaTNNkcmq2KVPXxMQ0utdLT/fSGfl6xrVSpFLfpq663iXhcBsXxi7T020rSKtikUpn1zQDATh27Bg333wzhc5b+buvfZ3Ozk4OHDiAYRjs2HE7mrDcSLHFJJPzbqWAxaylIdW1gFIeTeDRhJs5ns8XKUjcooi1LCnLLgTSsvD4PLyROk5PqodcIUcsEmZyZop9+/Zx9uwIu+JxdO9mTKNM2TAwTYNdu3ZiVQyEplGpWITDYb71rWe4+4EH6naRyeQIBPwNo5lq7f2GYbhPbpZlEQj4yWZzeNDo6+thaipBf819XMeiZJqk8gsKcXuv7dOolqcencugeTT8Hj9pM4uZCjXMsG9E7VNhNVFNE4L+/k1uqfDqxeYP+F2fRygQYK5iUcgVSaezvHZ6ku1b+/jp60u6Ia/AJEN9MbCk2xvD49HccVXt61NTCfx+H17dY/uFgEKxRDabolgs89JLL/Hwww/z6quvMjg0RDAYZG5uDtO0ZXbHHXfw3HPPueXfpU5DH0xtK9N8oUhPT6frS4L6ci2AW6rE7rgYIhjwk5xL2Q2MMrkV8zYyWbvgZE9Pp1MVObtq/21d1+nd1PgBoBEXxi7XVdNdjpV6aSxmpSjXRvsZ3Nrv+iE0j+Y68YdiYeYLpTon+mI6Ojq47777OHLkv5mYmHDyeWwCgQCl4sJDyeSU3dtGIDAr5oqKuFAs1kVuTU0l0DRxzfX1uPbVXwuYzOTIlg3mSuU1lVHu6e4g1BHlzdRJhow4sWiYXbt28h8/sjvvWpbJ4cNH+OnPDvPiiy9ybvQ8P/7xIZ5++iucOfM+z33nBd5//yzPP/8Cp069Rz5vF9irbWyUTmeZdpoZNdo/2KaMX9u5nfZYBE0ThIJBSmWTsmHi0TQKOTscdW6mYJdEx56B6EIwMlV2czxqHaQjyTSVGtNnVA+vqDgymfpZQK05we/3MbC5173hZTI5hCbqntKqzvKo8zdhmvQ6N/uzFxeiru6/c2/d8vatfdx/515uvX64bv837BrmwvgEfr+PcqnIlv5eJiamuTB2me7Odubn05SNsu2LyhcwDJNYRwx/KMSRE9NUTIOnvvwlKtEoe/bs4fDhw9x9153k83nuvfcgBw4cQNM0PvenfwKaoKurE6/X4yqOxOwcjdA9C/kJ1d95sV2/VC5TyNnH3N4exR/wu7KLOBV6l6P2qV9oglgssmoYNaweal17zIPxzWsqn1L1Z62Fps3sjt8hm60/73SPxmxh5XyYhx76DIcOHeKuOz7GE3/5OF1dXezYsY0d2weZm5tz81TAbmvc19tNb28XA85vcMnJZ6nN/QC74nBt6fhiqXRN9vVQyqMJKpa0w3AjbZRzOTxAZ4fdN7m8TKIgwOjlcc4UTnNT8HoKxQI+f4Bz50ZJJBJuLP4jjzzC8ePHicfjxCJhfucTH+fxxx/jwoULHDhwwP178uRJtm61o62McHhNF1315vvByCiGYRAKBRno7yUQ8FHI25nFuq4znUi6HQTHJzNk0pJkouz28TaKOTKZnF13qViyk9gqy3fxW4xhmCsWv1ts1qquW9vHu4rXq7OjM0oltXARdkSWRm/df+defnDkONXJ4BunRusUy8jYJAG/n/lUhnyhyPjElBu14w/4nQq9dr+STT1d+HxexmYKvDtW5Lc/0kcsZveX39nTzbZtw2zfNkwkGmX//tvYtm07Ho9GMODHkpLxS5P4fXZYNNSX0QfbLHRh7DKTU7NY0nKbV1VLtARDIaamEmSd4obV4ovrwapY7swI7Jn0h0m1FtpaaVTjzTBMW/4Vyz3eitn8iDWPxkwDJe1ZJrelek1t3RrnEx+/l/98+RVe+Md/Yu/evZw+fYbnv/siVsU2W8JSU1q1P83A5l7GL03i89YHBxiG6c50Fz4z1pTceTWhHOZNsLg8yejMHGEklUplSYexKqViiROFU9zWsc9thwogLYtINEqpUKBYKtPf18O5VBaZTBJuC1Gx7GTEYMBPJBLGLJtO9dpAXRjrSDJN2Ci7F9WA40g3DBOzbGJaFfcmXC1HMhjf7M6YdN02kVSfRA3DJD87SlZG3dDhimVxZgpuuy6y5OZfrrRx/HyG39hpT+mTZ2cpt5kEQyGCwSCZVIpQtJ/j5zN8dMdC2OLZaY2d/TqVSpkTY7BnMIhPqyAt087REBoejxdN91IqZimVzSX1smqZnJrl9KUiv3nTTk6+f37V37Kvu43+Td1k56cplcrEomGKpTKxWMSt2GvX8/ISC4eZT6fJlkzGkrYirgYOjCTT9IR8hN85w//kc8TjceLxOG1tbTz55JN89rMPuXkAlmXR2RFjPpVx99cRixDNHF9SlK9qnlurSWctzufapkXNmIpgoftfs6YVN3ppBVar31bL4pDctXJh7LJb38w0Tc6n80seekbPjzOweRNCaHi9Ov/wzWd49NFHsSoVnv32t3n88cc4evQY8Xgcr67TFg6DXP6hcTkKhRKWVXFNV7W/nXKY/x9n9Pw4vZu6qQhBCsGOvuUvqBOFU/RmermYvUxbZyeldBrd66Wru5Op6VnKhkkul6NsGARDPgZ2DLm9MEyz4kYqeb32zEBo9VNgj9Obe/T8eN0Tzgcjo8Si9sVRe8OvWpxqL9Zqme7qfroifmLhBZvtO+eS9LbbF+5i5fHa+yk+dn0E3evhrbM5btjZj+7s5JfvZ7lt52YOvTXPwX3tzKRNjp/NMtDl4/p4iJm0SVc4wJ5BwbGRHPt3RkHzkS7Y6x3cFyKRmCXcFqir/7S4c5vl+IL2bNXIFZO099o3q4BHo5JKo3s8XJwzyJV1dvXW9F6QBTLZHJWKRaitjVgsglWx7Og3w2SgvxfDMEhnck5NrwXFUZtVXxi5QBgYGhoCYGxsjHC4jccee4zx8XF8Xh2h2TetjGM+aWsLEQ6HuDg+yVxwO301pdZTqQzRiG36W8sNvlqiBRbyUhpFmPkDdtXXufmlmdWr4fP51qw4ZmbnyOcLDMY3L6s4ameT1XNxLQpwjW1tGqJ5tGW7/mXSOYYHB+ryo/7s859zlnT+/Aufp1zKc+Pu68jMJwmGu5cojunpBD3dnWvKIF9Lz5OrHWW2agLLMVtVolEKEnZ0t8Oi7NzxVHbJdrrHQyAYJDc3R8VpwFMqlunsiFEul9k+HKcSjRL2B0Hk8ft9RONhdCeD9fV3LnLp0hSxSLjO1AB2vsRIMs3w0BbyhcKSfcfaY66foa+3Z4nDcXo60TBfpJap+TJ7tnVSNpZGkR3c107I78EyDfZtb6Mwl8J08i72DocxSnkO7rMVYE9U5+C+doa6pfte03S8lLh9dxRd2Bdju7/MwX3tGKU8sWjUnc5Xbyy1F//IzBznUllS+bzd0rZUZmvIT9gosyUWxuPR2LSpi8EuO0hgMq258sjmFlq8JhJJJqdm3ZakXqcigEf38PZFezyxoMY9e3vrFIdMJukpFpno78Xn1enq6kTTNEqlEpcvX2IwvpmyYVIqFalUTCoVi8H4ZryOqXB4aAt9vd34E790vzMSbqursrsatc5Zf8BPe7vTureGS5enME0Tf8C/7CwZbBOMtCQTE9OMX5pcYq9fiWotrp7ujhWVgOV0hay+wL55t8cidX68RizufljddjUanTu1fhCfT1+1RAtAvmjSUzmH5tFcJznYSnvTpi6KpXp5GQ2umWDQ7/pDbAvAtWn9UWarJhBCzAAXmtysG5hdda3/HyhZ2Cg52Cg5LFCVxaCUcvUSDVcBSnlsMEKIo1LKW1p9HFcDShY2Sg42Sg4LXIuyUGYrhUKhUDSNUh4KhUKhaBqlPDaeZ1t9AFcRShY2Sg42Sg4LXHOyUD4PhUKhUDSNmnkoFAqFommU8lgnQohPCSHeFUJYQohbaj4/KIQ4JoQ46fy9x/k8JIT4oRDiPWe7v1nme71CiO84258WQjxxpca0HjZQDp8RQhyveVlCiL2N1r1a2ChZOOveKIR41VnvpBBi5eScFrKB58SQEKJQc048c6XGtB428nxw1o8LIbJCiC9u9FgaUm1Sr17NvYBfB3YBh4Fbaj7fB2x2lvcAl5zlEHC3s+wDfg7c1+B7Pw18r2ab88BQq8d7peWwaB83AOdaPdYWnhM68DZwk/O+C/C0erwtkMMQ8E6rx9dqOdR8z78C/wx8sRXjU+VJ1omU8jQs9E6u+fytmrfvAgEhhF9KmQd+5qxTFkK8CWxhKRJoE0LoQBAoA+kG610VbKAcavkj4MUP7aA3iA2Uxb3A21LKE866jcsnXyVcoXPiqmcj5SCE+F3gHLB6ev0GocxWG8uDwFtSyrpymUKIduB+4CcNtvkX7BNiAhgDvialXFu96quX9cihlj/gGlAea2Q9stgJSCHEy0KIN4UQj12B49xo1ntODAsh3hJCHBFC3L7RB3kFaFoOQog24HHgqStyhMugZh4rIIR4BWhUQe6vpJT/vsq2u4G/xX5qrP1cx74R/r2U8lyDTW8DKsBmoAP4uRDilWXWvSK0SA7V9fYDeSllM12eNowWyUIHfgu4FcgDPxFCHJNSrqZ0N4wWyWECiEspE0KIm4HvCyF2SylbNjNvkRyeAr4upcwuntVcSZTyWAEp5YH1bCeE2AL8G/DHUsqzi/79LPCBlPIby2z+aeC/pJQGMC2E+AVwC/YUtSW0SA5V/pCraNbRIlmMA0eklLPOd/0I+Airz9g2jFbIwXk6LznLx4QQZ7FnZUfXcywfBi06H/YDvy+E+CrQDlhCiKKU8pvrOZb1osxWHzLOdPOHwBNSyl8s+t/TQAz4wgpfMQbcI2zagI8C723U8W4UH4IcEEJowKeA723UcV4JPgRZvAzc6ETj6MCdwKmNOt6N4leVgxCiRwjhcZa3AdfRwoeq9fKrykFKebuUckhKOQR8A/jKlVYc1QNRr/VFUvwe9hNhCZgCXnY+/xK2z+J4zWsTtuNLAqdrPn/U2eaTwF87y2HsCIp3sW8Qf9HqsbZCDs77u4DXWj3Gq0QWDznnxDvAV1s91lbIAds/8C5wAngTuL/VY23V+VCzjy/TomgrlWGuUCgUiqZRZiuFQqFQNI1SHgqFQqFoGqU8FAqFQtE0SnkoFAqFommU8lAoFApF0yjloVAoFIqmUcpDoVAoFE2jlIdCoVAomuZ/Ac/I2MiSQex1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24393523e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterClubs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
